Method and apparatus for processing data in a multiple-input multiple-output (MIMO) communication system utilizing channel state information

ABSTRACT

Techniques to “successively” process received signals at a receiver unit in a MIMO system to recover transmitted data, and to “adaptively” process data at a transmitter unit based on channel state information available for the MIMO channel. A successive cancellation receiver processing technique is used to process the received signals and performs a number of iterations to provide decoded data streams. For each iteration, input (e.g., received) signals for the iteration are processed to provide one or more symbol streams. One of the symbol streams is selected and processed to provide a decoded data stream. The interference due to the decoded data stream is approximately removed (i.e., canceled) from the input signals provided to the next iteration. The channel characteristics are estimated and reported back to the transmitter system and used to adjust (i.e., adapt) the processing (e.g., coding, modulation, and so on) of data prior to transmission.

CLAIM OF PRIORITY UNDER 35 U.S.C. §120

The present Application for Patent is a Continuation and claims priorityto patent application Ser. No. 09/854,235 entitled “Method and Apparatusfor Processing Data in a Multiple-Input Multiple-Output (MIMO)Communication System Utilizing Channel Station Information” filed May11, 2001, now allowed, and assigned to the assignee hereof and herebyexpressly incorporated by reference herein.

BACKGROUND

1. Field

The present invention relates generally to data communication, and morespecifically to a novel and improved method and apparatus for processingdata in a multiple-input multiple-output (MIMO) communication systemutilizing channel state information to provide improved systemperformance.

2. Background

Wireless communication systems are widely deployed to provide varioustypes of communication such as voice, data, and so on. These systems maybe based on code division multiple access (CDMA), time division multipleaccess (TDMA), orthogonal frequency division multiplex (OFDM), or someother multiplexing techniques. OFDM systems may provide high performancefor some channel environments.

In a terrestrial communication system (e.g., a cellular system, abroadcast system, a multi-channel multi-point distribution system(MMDS), and others), an RF modulated signal from a transmitter unit mayreach a receiver unit via a number of transmission paths. Thecharacteristics of the transmission paths typically vary over time dueto a number of factors such as fading and multipath.

To provide diversity against deleterious path effects and improveperformance, multiple transmit and receive antennas may be used for datatransmission. If the transmission paths between the transmit and receiveantennas are linearly independent (i.e., a transmission on one path isnot formed as a linear combination of the transmissions on other paths),which is generally true to at least an extent, then the likelihood ofcorrectly receiving a data transmission increases as the number ofantennas increases. Generally, diversity increases and performanceimproves as the number of transmit and receive antennas increases.

A multiple-input multiple-output (MIMO) communication system employsmultiple (N_(T)) transmit antennas and multiple (N_(R)) receive antennasfor data transmission. A MIMO channel formed by the N_(T) transmit andN_(R) receive antennas may be decomposed into N_(C) independentchannels, with N_(C)<min {N_(T), N_(R)}. Each of the N_(C) independentchannels is also referred to as a spatial subchannel of the MIMO channeland corresponds to a dimension. The MIMO system can provide improvedperformance (e.g., increased transmission capacity) if the additionaldimensionalities created by the multiple transmit and receive antennasare utilized.

There is therefore a need in the art for techniques to process a datatransmission at both the transmitter and receiver units to takeadvantage of the additional dimensionalities created by a MIMO system toprovide improved system performance.

SUMMARY

Aspects of the invention provide techniques to process the receivedsignals at a receiver unit in a multiple-input multiple-output (MIMO)system to recover the transmitted data, and to adjust the dataprocessing at a transmitter unit based on estimated characteristics of aMIMO channel used for data transmission. In an aspect, a “successivecancellation” receiver processing technique (described below) is used toprocess the received signals. In another aspect, the channelcharacteristics are estimated and reported back to the transmittersystem and used to adjust (i.e., adapt) the processing (e.g., coding,modulation, and so on) of data prior to transmission. Using acombination of the successive cancellation receiver processing techniqueand adaptive transmitter processing technique, high performance may beachieved for the MIMO system.

A specific embodiment of the invention provides a method for sendingdata from a transmitter unit to a receiver unit in a MIMO communicationsystem. In accordance with the method, at the receiver unit, a number ofsignals are initially received via a number of receive antennas, witheach received signal comprising a combination of one or more signalstransmitted from the transmitter unit. The received signals areprocessed in accordance with a successive cancellation receiverprocessing technique to provide a number of decoded data streams, whichare estimates of the data streams transmitted from the transmitter unit.Channel state information (CSI) indicative of characteristics of a MIMOchannel used to transmit the data steams are also determined andtransmitted back to the transmitter unit. At the transmitter unit, eachdata stream is adaptively processed prior to transmission over the MIMOchannel in accordance with the received CSI.

The successive cancellation receiver processing scheme typicallyperforms a number of iterations to provide the decoded data streams, oneiteration for each decoded data stream. For each iteration, a number ofinput signals for the iteration are processed in accordance with aparticular linear or non-linear processing scheme to provide one or moresymbol streams. One of the symbol streams is then selected and processedto provide a decoded data stream. A number of modified signals are alsoderived based on the input signals, with the modified signals havingcomponents due to the decoded data stream approximately removed (i.e.,canceled). The input signals for a first iteration are the receivedsignals and the input signals for each subsequent iteration are themodified signals from a preceding iteration.

Various linear and non-linear processing schemes may be used to processthe input signals. For a non-dispersive channel (i.e., with flatfading), a channel correlation matrix inversion (CCMI) technique, aminimum mean square error (MMSE) technique, or some other techniques maybe used. And for a time-dispersive channel (i.e., with frequencyselective fading), an MMSE linear equalizer (MMSE-LE), a decisionfeedback equalizer (DFE), a maximum-likelihood sequence estimator(MLSE), or some other techniques may be used.

The available CSI may include, for example, thesignal-to-noise-plus-interference (SNR) of each transmission channel tobe used for data transmission. At the transmitter unit, the data foreach transmission channel may be coded based on the CSI associated withthat channel, and the coded data for each transmission channel mayfurther be modulated in accordance with a modulation scheme selectedbased on the CSI.

The invention further provides methods, systems, and apparatus thatimplement various aspects, embodiments, and features of the invention,as described in further detail below.

BRIEF DESCRIPTION OF THE DRAWINGS

The features, nature, and advantages of the present invention willbecome more apparent from the detailed description set forth below whentaken in conjunction with the drawings in which like referencecharacters identify correspondingly throughout and wherein:

FIG. 1 is a diagram of a multiple-input multiple-output (MIMO)communication system capable of implementing various aspects andembodiments of the invention;

FIG. 2 is a block diagram of an embodiment of a MIMO transmitter systemcapable of processing data for transmission based on the available CSI;

FIG. 3 is a block diagram of an embodiment of a MIMO transmitter systemwhich utilizes orthogonal frequency division modulation (OFDM);

FIG. 4 is a flow diagram illustrating a successive cancellation receiverprocessing technique to process N_(R) received signals to recover N_(T)transmitted signals;

FIG. 5 is a block diagram of a receiver system capable of implementingvarious aspects and embodiments of the invention;

FIGS. 6A, 6B, and 6C are block diagrams of three channel MIMO/dataprocessors, which are capable of implementing a CCMI technique, a MMSEtechnique, and a DFE technique, respectively;

FIG. 7 is a block diagram of an embodiment of a receive (RX) dataprocessor;

FIG. 8 is a block diagram of an interference canceller; and

FIGS. 9A, 9B, and 9C are plots that illustrate the performance forvarious receiver and transmitter processing schemes.

DETAILED DESCRIPTION

FIG. 1 is a diagram of a multiple-input multiple-output (MIMO)communication system 100 capable of implementing various aspects andembodiments of the invention. System 100 includes a first system 110 incommunication with a second system 150. System 100 can be operated toemploy a combination of antenna, frequency, and temporal diversity(described below) to increase spectral efficiency, improve performance,and enhance flexibility. In an aspect, system 150 can be operated todetermine the characteristics of a MIMO channel and to report channelstate information (CSI) indicative of the channel characteristics thathave been determined in this way back to system 110, and system 110 canbe operated to adjust the processing (e.g., encoding and modulation) ofdata prior to transmission based on the available CSI. In anotheraspect, system 150 can be operated to process the data transmission fromsystem 110 in a manner to provide high performance, as described infurther detail below.

At system 110, a data source 112 provides data (i.e., information bits)to a transmit (TX) data processor 114, which encodes the data inaccordance with a particular encoding scheme, interleaves (i.e.,reorders) the encoded data based on a particular interleaving scheme,and maps the interleaved bits into modulation symbols for one or moretransmission channels used for transmitting the data. The encodingincreases the reliability of the data transmission. The interleavingprovides time diversity for the coded bits, permits the data to betransmitted based on an average signal-to-noise-plus-interference-ratio(SNR) for the transmission channels used for the data transmission,combats fading, and further removes correlation between coded bits usedto form each modulation symbol. The interleaving may further providefrequency diversity if the coded bits are transmitted over multiplefrequency subchannels. In an aspect, the encoding, interleaving, andsymbol mapping (or a combination thereof) are performed based on the CSIavailable to system 110, as indicated in FIG. 1.

The encoding, interleaving, and symbol mapping at transmitter system 110can be performed based on numerous schemes. One specific scheme isdescribed in U.S. patent application Ser. No. 09/776,073, entitled“CODING SCHEME FOR A WIRELESS COMMUNICATION SYSTEM,” filed Feb. 1, 2001,assigned to the assignee of the present application and incorporatedherein by reference. Another scheme is described in further detailbelow.

MIMO system 100 employs multiple antennas at both the transmit andreceive ends of the communication link. These transmit and receiveantennas may be used to provide various forms of spatial diversity(i.e., antenna diversity), including transmit diversity and receivediversity. Spatial diversity is characterized by the use of multipletransmit antennas and one or more receive antennas. Transmit diversityis characterized by the transmission of data over multiple transmitantennas. Typically, additional processing is performed on the datatransmitted from the transmit antennas to achieved the desireddiversity. For example, the data transmitted from different transmitantennas may be delayed or reordered in time, coded and interleavedacross the available transmit antennas, and so on. Receive diversity ischaracterized by the reception of the transmitted signals on multiplereceive antennas, and diversity is achieved by simply receiving thesignals via different signal paths.

System 100 may be operated in a number of different communication modes,with each communication mode employing antenna, frequency, or temporaldiversity, or a combination thereof. The communication modes mayinclude, for example, a “diversity” communication mode and a “MIMO”communication mode. The diversity communication mode employs diversityto improve the reliability of the communication link. In a commonapplication of the diversity communication mode, which is also referredto as a “pure” diversity communication mode, data is transmitted fromall available transmit antennas to a recipient receiver system. The purediversity communications mode may be used in instances where the datarate requirements are low or when the SNR is low, or when both are true.The MIMO communication mode employs antenna diversity at both ends ofthe communication link (i.e., multiple transmit antennas and multiplereceive antennas) and is generally used to both improve the reliabilityand increase the capacity of the communication link. The MIMOcommunication mode may further employ frequency and/or temporaldiversity in combination with the antenna diversity.

System 100 may utilize orthogonal frequency division modulation (OFDM),which effectively partitions the operating frequency band into a numberof (N_(L)) frequency subchannels (i.e., frequency bins). At each timeslot (i.e., a particular time interval that may be dependent on thebandwidth of the frequency subchannel), a modulation symbol may betransmitted on each of the N_(L) frequency subchannels.

System 100 may be operated to transmit data via a number of transmissionchannels. As noted above, a MIMO channel may be decomposed into N_(C)independent channels, with N_(C)<min {N_(T), N_(R)}. Each of the N_(C)independent channels is also referred to as a spatial subchannel of theMIMO channel. For a MIMO system not utilizing OFDM, there is typicallyonly one frequency subchannel and each spatial subchannel may bereferred to as a “transmission channel.” For a MIMO system utilizingOFDM, each spatial subchannel of each frequency subchannel may bereferred to as a transmission channel.

A MIMO system can provide improved performance if the additionaldimensionalities created by the multiple transmit and receive antennasare utilized. While this does not necessarily require knowledge of CSIat the transmitter, increased system efficiency and performance arepossible when the transmitter is equipped with CSI, which is descriptiveof the transmission characteristics from the transmit antennas to thereceive antennas. The processing of data at the transmitter prior totransmission is dependent on whether or not CSI is available.

The available CSI may comprise, for example, thesignal-to-noise-plus-interference-ratio (SNR) of each transmissionchannel (i.e., the SNR for each spatial subchannel for a MIMO systemwithout OFDM, or the SNR for each spatial subchannel of each frequencysubchannel for a MIMO system with OFDM). In this case, data may beadaptively processed at the transmitter (e.g., by selecting the propercoding and modulation scheme) for each transmission channel based on thechannel's SNR.

For a MIMO system not employing OFDM, TX MIMO processor 120 receives anddemultiplexes the modulation symbols from TX data processor 114 andprovides a stream of modulation symbols for each transmit antenna, onemodulation symbol per time slot. And for a MIMO system employing OFDM,TX MIMO processor 120 provides a stream of modulation symbol vectors foreach transmit antenna, with each vector including N_(L) modulationsymbols for the N_(L) frequency subchannels for a given time slot. Eachstream of modulation symbols or modulation symbol vectors is receivedand modulated by a respective modulator (MOD) 122, and transmitted viaan associated antenna 124.

At receiver system 150, a number of receive antennas 152 receive thetransmitted signals and provide the received signals to respectivedemodulators (DEMOD) 154. Each demodulator 154 performs processingcomplementary to that performed at modulator 122. The modulation symbolsfrom all demodulators 154 are provided to a receive (RX) MIMO/dataprocessor 156 and processed to recover the transmitted data streams. RXMIMO/data processor 156 performs processing complementary to thatperformed by TX data processor 114 and TX MIMO processor 120 andprovides decoded data to a data sink 160. The processing by receiversystem 150 is described in further detail below.

The spatial subchannels of a MIMO system (or more generally, thetransmission channels in a MIMO system with or without OFDM) typicallyexperience different link conditions (e.g., different fading andmultipath effects) and may achieve different SNR. Consequently, thecapacity of the transmission channels may be different from channel tochannel. This capacity may be quantified by the information bit rate(i.e., the number of information bits per modulation symbol) that may betransmitted on each transmission channel for a particular level ofperformance (e.g., a particular bit error rate (BER) or packet errorrate (PER)). Moreover, the link conditions typically vary with time. Asa result, the supported information bit rates for the transmissionchannels also vary with time. To more fully utilize the capacity of thetransmission channels, CSI descriptive of the link conditions may bedetermined (typically at the receiver unit) and provided to thetransmitter unit so that the processing can be adjusted (or adapted)accordingly. The CSI may comprise any type of information that isindicative of the characteristics of the communication link and may bereported via various mechanisms, as described in further detail below.For simplicity, various aspects and embodiments of the invention aredescribed below wherein the CSI comprises SNR. Techniques to determineand utilize CSI to provide improved system performance are describedbelow.

MIMO Transmitter System with CSI Processing

FIG. 2 is a block diagram of an embodiment of a MIMO transmitter system110 a, which does not utilize OFDM but is capable of adjusting itsprocessing based on CSI available to the transmitter system (e.g., asreported by receiver system 150). Transmitter system 110 a is oneembodiment of the transmitter portion of system 110 in FIG. 1. System110 a includes (1) a TX data processor 114 a that receives and processesinformation bits to provide modulation symbols and (2) a TX MIMOprocessor 120 a that demultiplexes the modulation symbols for the N_(T)transmit antennas.

In the specific embodiment shown in FIG. 2, TX data processor 114 aincludes a demultiplexer 208 coupled to a number of channel dataprocessors 210, one processor for each of the N_(C) transmissionchannels. Demultiplexer 208 receives and demultiplexes the aggregateinformation bits into a number of (up to N_(C)) data streams, one datastream for each of the transmission channels to be used for datatransmission. Each data stream is provided to a respective channel dataprocessor 210.

In the embodiment shown in FIG. 2, each channel data processor 210includes an encoder 212, a channel interleaver 214, and a symbol mappingelement 216. Encoder 212 receives and encodes the information bits inthe received data stream in accordance with a particular encoding schemeto provide coded bits. Channel interleaver 214 interleaves the codedbits based on a particular interleaving scheme to provide diversity. Andsymbol mapping element 216 maps the interleaved bits into modulationsymbols for the transmission channel used for transmitting the datastream.

Pilot data (e.g., data of known pattern) may also be encoded andmultiplexed with the processed information bits. The processed pilotdata may be transmitted (e.g., in a time division multiplexed (TDM)manner) in all or a subset of the transmission channels used to transmitthe information bits. The pilot data may be used at the receiver toperform channel estimation, as described below.

As shown in FIG. 2, the data encoding, interleaving, and modulation (ora combination thereof) may be adjusted based on the available CSI (e.g.,as reported by receiver system 150). In one coding and modulationscheme, adaptive encoding is achieved by using a fixed base code (e.g.,a rate ⅓ Turbo code) and adjusting the puncturing to achieve the desiredcode rate, as supported by the SNR of the transmission channel used totransmit the data. For this scheme, the puncturing may be performedafter the channel interleaving. In another coding and modulation scheme,different coding schemes may be used based on the reported CSI. Forexample, each of the data streams may be coded with an independent code.With this scheme, a “successive cancellation” receiver processing schememay be used to detect and decode the data streams to derive a morereliable estimate of the transmitted data streams, as described infurther detail below.

Symbol mapping element 216 can be designed to group sets of interleavedbits to form non-binary symbols, and to map each non-binary symbol intoa point in a signal constellation corresponding to a particularmodulation scheme (e.g., QPSK, M-PSK, M-QAM, or some other scheme)selected for the transmission channel. Each mapped signal pointcorresponds to a modulation symbol.

The number of information bits that may be transmitted for eachmodulation symbol for a particular level of performance (e.g., onepercent PER) is dependent on the SNR of the transmission channel. Thus,the coding and modulation scheme for each transmission channel may beselected based on the available CSI. The channel interleaving may alsobe adjusted based on the available CSI.

Table 1 lists various combinations of coding rate and modulation schemethat may be used for a number of SNR ranges. The supported bit rate foreach transmission channel may be achieved using any one of a number ofpossible combinations of coding rate and modulation scheme. For example,one information bit per modulation symbol may be achieved using (1) acoding rate of ½ and QPSK modulation, (2) a coding rate of ⅓ and 8-PSKmodulation, (3) a coding rate of ¼ and 16-QAM, or some other combinationof coding rate and modulation scheme. In Table 1, QPSK, 16-QAM, and64-QAM are used for the listed SNR ranges. Other modulation schemes suchas 8-PSK, 32-QAM, 128-QAM, and so on, may also be used and are withinthe scope of the invention. TABLE 1 SNR # of Information Modulation # ofCoded Coding Range Bits/Symbol Symbol Bits/Symbol Rate 1.5-4.4 1 QPSK 21/2 4.4-6.4 1.5 QPSK 2 3/4  6.4-8.35 2 16-QAM 4 1/2 8.35-10.4 2.5 16-QAM4 5/8 10.4-12.3 3 16-QAM 4 3/4  12.3-14.15 3.5 64-QAM 6  7/1214.15-15.55 4 64-QAM 6 2/3 15.55-17.35 4.5 64-QAM 6 3/4 >17.35 5 64-QAM6 5/6

The modulation symbols from TX data processor 114 a are provided to a TXMIMO processor 120 a, which is one embodiment of TX MIMO processor 120in FIG. 1. Within TX MIMO processor 120 a, a demultiplexer 222 receives(up to) N_(C) modulation symbol streams from N_(C) channel dataprocessors 210 and demultiplexes the received modulation symbols into anumber of (N_(T)) modulation symbol streams, one stream for each antennaused to transmit the modulation symbols. Each modulation symbol streamis provided to a respective modulator 122. Each modulator 122 convertsthe modulation symbols into an analog signal, and further amplifies,filters, quadrature modulates, and upconverts the signal to generate amodulated signal suitable for transmission over the wireless link.

MIMO Transmitter System with OFDM

FIG. 3 is a block diagram of an embodiment of a MIMO transmitter system110 c, which utilizes OFDM and is capable of adjusting its processingbased on the available CSI. Within a TX data processor 114 c, theinformation bits to be transmitted are demultiplexed into a number of(up to N_(L)) frequency subchannel data streams, one stream for each ofthe frequency subchannels to be used for the data transmission. Eachfrequency subchannel data stream is provided to a respective frequencysubchannel data processor 310.

Each data processor 310 processes data for a respective frequencysubchannel of the OFDM system. Each data processor 310 may beimplemented similar to TX data processor 114 a shown in FIG. 2. For thisdesign, data processor 310 includes a demultiplexer that demultiplexesthe frequency subchannel data stream into a number of data substreams,one substream for each spatial subchannel used for the frequencysubchannel. Each data substream is then encoded, interleaved, and symbolmapped by a respective channel data processor to generate modulationsymbols for that particular transmission channel (i.e., that spatialsubchannel of that frequency subchannel). The coding and modulation foreach transmission channel may be adjusted based on the available CSI(e.g., reported by the receiver system). Each frequency subchannel dataprocessor 310 thus provides (up to) N_(C) modulation symbol streams for(up to) N_(C) spatial subchannels.

For a MIMO system utilizing OFDM, the modulation symbols may betransmitted on multiple frequency subchannels and from multiple transmitantennas. Within a MIMO processor 120 c, the N_(C) modulation symbolstreams from each data processor 310 are provided to a respectivechannel MIMO processor 322, which processes the received modulationsymbols based on the available CSI.

Each channel MIMO processor 322 demultiplexes the N_(C) modulationsymbols for each time slot into N_(T) modulation symbols for the N_(T)transmit antennas. Each combiner 324 receives the modulation symbols forup to N_(L) frequency subchannels, combines the symbols for each timeslot into a modulation symbol vector V, and provides the modulationsymbol vector to the next processing stage (i.e., a respective modulator122).

MIMO processor 120 c thus receives and processes the modulation symbolsto provide N_(T) modulation symbol vectors, V₁ through V_(Nt), onemodulation symbol vector for each transmit antenna. Each modulationsymbol vector V covers a single time slot, and each element of themodulation symbol vector V is associated with a specific frequencysubchannel having a unique subcarrier on which the modulation symbol isconveyed.

FIG. 3 also shows an embodiment of modulator 122 for OFDM. Themodulation symbol vectors V₁ through VNt from MIMO processor 120 c areprovided to modulators 122 a through 122 t, respectively. In theembodiment shown in FIG. 3, each modulator 122 includes an inverse FastFourier Transform (IFFT) 320, cycle prefix generator 322, and anupconverter 324.

IFFT 320 converts each received modulation symbol vector into itstime-domain representation (which is referred to as an OFDM symbol)using IFFT. IFFT 320 can be designed to perform the IFFT on any numberof frequency subchannels (e.g., 8, 16, 32, and so on). In an embodiment,for each modulation symbol vector converted to an OFDM symbol, cycleprefix generator 322 repeats a portion of the time-domain representationof the OFDM symbol to form a “transmission symbol” for a specifictransmit antenna. The cyclic prefix insures that the transmission symbolretains its orthogonal properties in the presence of multipath delayspread, thereby improving performance against deleterious multipatheffects. The implementation of IFFT 320 and cycle prefix generator 322is known in the art and not described in detail herein.

The time-domain representations from each cyclic prefix generator 322(i.e., the transmission symbols for each antenna) are then processed(e.g., converted into an analog signal, modulated, amplified, andfiltered) by upconverter 324 to generate a modulated signal, which isthen transmitted from the respective antenna 124.

OFDM modulation is described in further detail in a paper entitled“Multicarrier Modulation for Data Transmission: An Idea Whose Time HasCome,” by John A. C. Bingham, IEEE Communications Magazine, May 1990,which is incorporated herein by reference.

FIGS. 2 and 3 show two designs of a MIMO transmitter capable ofimplementing various aspects of the invention. Other transmitter designsmay also be implemented and are within the scope of the invention. Someof these transmitter designs are described in further detail in U.S.patent application Ser. No. 09/532,492, entitled “HIGH EFFICIENCY, HIGHPERFORMANCE COMMUNICATIONS SYSTEM EMPLOYING MULTI-CARRIER MODULATION,”filed Mar. 22, 2000, the aforementioned U.S. patent application Ser. No.09/776,073, and U.S. patent application Ser. No. 09/816,481 “METHOD ANDAPPARATUS FOR UTILIZING CHANNEL STATE INFORMATION IN A WIRELESSCOMMUNICATION SYSTEM,” filed Mar. 23, 2001, all assigned to the assigneeof the present application and incorporated herein by reference. Thesepatent applications describe MIMO processing and CSI processing infurther detail.

In general, transmitter system 110 codes and modulates data for eachtransmission channel based on information descriptive of that channel'stransmission capability. This information is typically in the form ofCSI. The CSI for the transmission channels used for data transmission istypically determined at the receiver system and reported back to thetransmitter system, which then uses the information to adjust the codingand modulation accordingly. The techniques described herein areapplicable for multiple parallel transmission channels supported byMIMO, OFDM, or any other communication scheme (e.g., a CDMA scheme)capable of supporting multiple parallel transmission channels.

MIMO Receiver System

Aspects of the invention provide techniques to (1) process the receivedsignals at a receiver system in a MIMO system based on a successivecancellation receiver processing scheme to recover the transmitted data,and (2) adjust the data processing at a transmitter system based onestimated characteristics of the MIMO channel. In an aspect, thesuccessive cancellation receiver processing technique (described below)is used to process the received signals. In another aspect, the channelcharacteristics are estimated at the receiver system and reported backto the transmitter system, which uses the information to adjust (i.e.,adapt) the data processing (e.g., coding, modulation, and so on). Usinga combination of the successive cancellation receiver processingtechnique and adaptive transmitter processing technique, highperformance may be achieved for the MIMO system.

FIG. 4 is a flow diagram illustrating the successive cancellationreceiver processing technique to process N_(R) received signals torecover N_(T) transmitted signals. For simplicity, the followingdescription for FIG. 4 assumes that (1) the number of transmissionchannels (i.e., spatial subchannels for a MIMO system not utilizingOFDM) is equal to the number of transmit antenna (i.e., N_(C)=N_(T)) and(2) one independent data stream is transmitted from each transmitantenna.

Initially, the receiver system performs linear and/or non-linearspace-processing on the N_(R) received signals to attempt to separatethe multiple transmitted signals included in the received signals, atstep 412. Linear spatial processing may be performed on the receivedsignals if the MIMO channel is “non-dispersive” (i.e., frequencynon-selective or flat fading). It may also be necessary or desirable toperform additional linear or non-linear temporal processing (i.e.,equalization) on the received signals if the MIMO channel is“time-dispersive” (i.e., frequency selective fading). The spatialprocessing may be based on a channel correlation matrix inversion (CCMI)technique, a minimum mean square error (MMSE) technique, or some othertechnique. The space-time processing may be based on an MMSE linearequalizer (MMSE-LE), a decision feedback equalizer (DFE), amaximum-likelihood sequence estimator (MLSE), or some other technique.Some of these spatial and space-time processing techniques are describedin further detail below. The amount of achievable signal separation isdependent on the amount of correlation between the transmitted signals,and greater signal separation may be obtained if the transmitted signalsare less correlated.

The initial spatial or space-time processing step provides N_(T)“post-processed” signals that are estimates of the N_(T) transmittedsignals. The SNRs for the N_(T) post-processed signals are thendetermined, at step 414. The SNR may be estimated as described infurther detail below. In one embodiment, the SNRs are ranked in orderfrom highest to lowest SNR, and the post-processed signal having thehighest SNR is selected and further processed (i.e., “detected”) toobtain a decoded data stream, at step 416. The detection typicallyincludes demodulating, deinterleaving, and decoding the selectedpost-processed signal. The decoded data stream is an estimate of thedata stream transmitted on the transmitted signal being recovered inthis iteration. The particular post-processed signal to be detected mayalso be selected based on some other scheme (e.g., the particular signalmay be specifically identified by the transmitter system).

At step 418, a determination is made whether or not all transmittedsignals have been recovered. If all transmitted signals have beenrecovered, then the receiver processing terminates. Otherwise, theinterference due to the decoded data stream is removed from the receivedsignals to generate “modified” signals for the next iteration to recoverthe next transmitted signal.

At step 420, the decoded data stream is used to form an estimate of theinterference presented by the transmitted signal corresponding to thedecoded data stream on each of the received signals. The interferencecan be estimated by first re-encoding the decoded data stream,interleaving the re-encoded data, and symbol mapping the interleaveddata (using the same coding, interleaving, and modulation schemes usedat the transmitter for this data stream) to obtain a stream of“remodulated” symbols. The remodulated symbol stream is an estimate ofthe modulation symbol stream previously transmitted from one of theN_(T) transmit antennas and received by the N_(R) received antennas.Thus, the remodulated symbol stream is convolved by each of N_(R)elements in an estimated channel response vector h _(i) to derive N_(R)interference signals due to the recovered transmitted signal. The vectorh _(i) is a particular column of a (N_(R)×N_(T)) channel coefficientmatrix H, which represents an estimate of the MIMO channel response forthe N_(T) transmit antennas and N_(R) receive antennas at a specifictime and which may be derived based on pilot signals transmitted alongwith the data. The N_(R) interference signals are then subtracted fromthe N_(R) corresponding received signals to derive N_(R) modifiedsignals, at step 422. These modified signals represent the signals atthe received antennas if the components due to the decoded data streamhad not been transmitted (i.e., assuming that the interferencecancellation was effectively performed).

The processing performed in steps 412 through 416 is then repeated onthe N_(R) modified signals (instead of the N_(R) received signals) torecover another transmitted signal. Steps 412 through 416 are thusrepeated for each transmitted signal to be recovered, and steps 420 and422 are performed if there is another transmitted signal to berecovered.

The successive cancellation receiver processing technique thus performsa number of iterations, one iteration for each transmitted signal to berecovered. Each iteration (except for the last) performs a two-partprocessing to recover one of the transmitted signals and to generate themodified signals for the next iteration. In the first part, spatialprocessing or space-time processing is performed on the N_(R) receivedsignals to provide N_(R) post-processed signals, and one of thepost-processed signals is detected to recover the data streamcorresponding to this transmitted signal. In the second part (whichneeds not be performed for the last iteration), interference due to thedecoded data stream is canceled from the received signals to derivemodified signals having the recovered component removed. Initially, theinput signals for the first iteration are the received signals, whichmay be expressed as: $\begin{matrix}{{{\underset{\_}{r}}^{1} = {\underset{\_}{r} = \begin{bmatrix}r_{1} \\r_{2} \\\vdots \\r_{N_{R}}\end{bmatrix}}},} & {{Eq}\quad(1)}\end{matrix}$where r is the vector of N_(R) received signals and r ¹ is the vector ofN_(R) input signals for the first iteration of the successivecancellation receiver processing scheme. These input signals arelinearly or non-linearly processed to provide post-processed signals,which may be expressed as: $\begin{matrix}{{\underset{\_}{x}}^{1} = {\begin{bmatrix}x_{1}^{1} \\x_{2}^{1} \\\vdots \\x_{N_{T}}^{1}\end{bmatrix}.}} & {{Eq}\quad(2)}\end{matrix}$where x ¹ is the vector of N_(R) post-processed signals from the firstiteration. The SNR of the post-processed signals may be estimated, whichmay be expressed as:γ ¹=[γ₁ ¹, γ₁ ¹, . . . , γ_(N) ¹ _(T)].  Eq (3)

One of the post-processed signals is selected for further processing(e.g., the post-processed signal with the highest SNR) to provide adecoded data stream. This decoded data stream is then used to estimatethe interference i ¹ generated by the recovered signal, which may beexpressed as: $\begin{matrix}{{\underset{\_}{\hat{i}}}^{1} = {\begin{bmatrix}{\hat{i}}_{1}^{1} \\{\hat{i}}_{2}^{1} \\\vdots \\{\hat{i}}_{N_{R}}^{1}\end{bmatrix}.}} & {{Eq}\quad(4)}\end{matrix}$The interference î ¹ is then subtracted from the input signal vector r ¹for this iteration to derive modified signals that comprise the inputsignal vector r ² for the next iteration. The interference cancellationmay be expressed as: $\begin{matrix}{{\underset{\_}{r}}^{2} = {{{\underset{\_}{r}}^{1} - {\underset{\_}{\hat{i}}}^{1}} = {\begin{bmatrix}{r_{1}^{1} - {\hat{i}}_{1}^{1}} \\{r_{2}^{1} - {\hat{i}}_{2}^{1}} \\\vdots \\{r_{N_{R}}^{1} - {\hat{i}}_{N_{R}}^{1}}\end{bmatrix}.}}} & {{Eq}\quad(5)}\end{matrix}$

The same process is then repeated for the next iteration, with thevector r ² comprising the input signals for this iteration.

With the successive cancellation receiver processing scheme, onetransmitted signal is recovered for each iteration, and the SNR for thei-th transmitted signal recovered in the k-th iteration, γ_(i) ^(k), maybe provided as the CSI for the transmission channel used to transmitthis recovered signal. As an example, if the first post-processed signalx₁ ¹ is recovered in the first iteration, the second post-processedsignal x₂ ² is recovered in the second iteration, and so on, and theN_(T)-th post-processed signal x_(N) _(T) ^(N) _(T) is recovered in thelast iteration, then the CSI that may be reported for these recoveredsignals may be expressed as: γ=[γ₁ ¹, γ₂ ², . . . , γ_(N) _(T) ^(N) _(T)].

Using the successive cancellation receiver processing technique, theoriginal N_(R) received signals are thus successively processed torecover one transmitted signal at a time. Moreover, each recoveredtransmitted signal is removed (i.e., canceled) from the received signalsprior to the processing to recover the next transmitted signal. If thetransmitted data streams can be decoded without error (or with minimalerrors) and if the channel response estimate is reasonably accurately,then the cancellation of interference due to previously recoveredtransmitted signals from the received signals is effective. Theinterference cancellation typically improves the SNR of each transmittedsignal to be subsequently recovered. In this way, higher performance maybe achieved for all transmitted signals (possibly except for the firsttransmitted signal to be recovered).

The possible improvement in SNR for the recovered transmitted signalsusing the successive cancellation receiver processing technique may beillustrated by an example. In this example, a pair of cross-polarizedantennas is employed at both the transmitter and receiver, the MIMOchannel is line-of-sight, and four independent data streams aretransmitted on the vertical and horizontal components of the pair ofcross-polarized transmit antennas. For simplicity, it is assumed thatthe cross-polarization isolation is perfect so that the vertical andhorizontal components do not interfere with one another at the receiver.

The receiver initially receives four signals on the vertical andhorizontal components of the pair of cross-polarized received antennasand processes these four received signals. The received signals on thevertical elements of the cross-polarized antennas are highly correlated,and the received signals on the horizontal elements are similarly highlycorrelated.

When there is a strong linear dependence between two or moretransmit-receive antenna pairs composing the MIMO channel, the abilityto null interference is compromised. In this case, the linear spatialprocessing will be unsuccessful at separating the four independent datastreams transmitted on the vertical and horizontal components of thepair of cross-polarized antennas. Specifically, the vertical componenton each cross-polarized transmit antenna interferes with the verticalcomponent on the other cross-polarized transmit antenna, and similarinterference is experienced on the horizontal component. Thus, theresulting SNR for each of the four transmitted signals will be poor dueto the correlated interference from the other antenna with the samepolarization. As a result, the capacity of the transmitted signals basedonly on linear spatial processing will be severely constrained by thecorrelated interference signal.

When the eigenmodes for this example MIMO channel are examined, it canbe seen that there are only two non-zero eigenmodes (i.e., the verticaland horizontal polarizations). A “full-CSI” processing scheme would thentransmit only two independent data streams using these two eigenmodes.The capacity achieved in this case can be expressed as:Capacity=2·log ₂(1+λ_(i)/σ²),where λ₁/σ² is the ratio of received signal power to thermal noise powerfor the i-th eigenmode. Thus, the capacity of the full-CSI processingscheme for this example MIMO channel is identical to the capacity of twoparallel additive white Gaussian noise (AWGN) channels, each having anSNR given by λ_(i)/σ².

With the successive cancellation receiver processing technique, thelinear spatial processing performed in step 412 initially results in theSNR for each of the four transmitted signals being 0 dB or less (due tothe noise plus interference from the other transmitted signal on thesame polarization). The overall capacity would be poor if no additionalreceiver processing is performed.

However, by applying successive spatial processing and interferencecancellation, the SNR of subsequently recovered transmitted signals canbe improved. For example, the first transmitted signal to be recoveredmay be the vertical polarization from the first cross-polarized transmitantenna. If it is assumed that the interference cancellation iseffectively performed (i.e., zero or minimal decision errors andaccurate channel estimates), then this signal no longer (or minimally)interferes with the remaining three (not yet recovered) transmittedsignals. Removing this vertical polarization interference improves theSNR on the other not yet recovered signal transmitted on the verticalpolarization. The cross-polarization isolation was assumed to be perfectfor this simple example, and the two signals transmitted on thehorizontal polarization do not interference with the signals transmittedon the vertical polarization. Thus, with effective interferencecancellation, the signal transmitted on the vertical polarization of thesecond cross-polarized transmit antenna may be recovered at an SNR thatis (theoretically) limited by thermal noise power.

In the above example, removing the interference from the verticalpolarization does not impact the SNR of the two signals transmitted onthe horizontal polarizations. Thus, the successive spatial processingand interference cancellation are similarly applied for the two signalstransmitted on the horizontal polarization. This results in the firstrecovered signal on the horizontal polarization having a low SNR and thesecond recovered signal on the horizontal polarization having an SNRthat is also (theoretically) limited by thermal noise.

As a result of performing successive spatial processing and interferencecancellation, the two transmitted signals with low SNR contribute littleto the total capacity, but the two transmitted signals with high SNRcontribute in a significant manner to the total capacity.

Non-Dispersive and Dispersive Channels

Different receive and (possibly) transmit processing schemes may be useddepending on the characteristics of the MIMO channel, which may becharacterized as either non-dispersive or dispersive. A non-dispersiveMIMO channel experiences flat fading (i.e., frequency non-selectivefading), which may be more likely when the system bandwidth is narrow. Adispersive MIMO channel experiences frequency non-selective fading(e.g., different amount of attenuation across the system bandwidth),which may be more likely when the system bandwidth is wide and forcertain operating conditions and environments. The successivecancellation receiver processing technique can be advantageously usedfor both non-dispersive and dispersive MIMO channels.

For a non-dispersive MIMO channel, linear spatial processing techniquessuch as CCMI and MMSE may be used to process the received signals priorto demodulation and decoding. These linear spatial processing techniquesmay be employed at the receiver to null out the undesired signals, or tomaximize the received signal-to-interference-plus-noise ratio of each ofthe constituent signals in the presence of noise and interference fromthe other signals. The ability to effectively null undesired signals oroptimize the signal-to-interference-plus-noise ratios depends upon thecorrelation in the channel coefficient matrix H that describes thechannel response between the transmit and receive antennas. Thesuccessive cancellation receiver processing technique (e.g., with CCMIor MMSE) can be advantageously used for non-dispersive MIMO channel.

For a dispersive MIMO channel, time dispersion in the channel introducesinter-symbol interference (ISI). To improve performance, a widebandreceiver attempting to recover a particular transmitted data streamwould need to ameliorate both “crosstalk” from the other transmittedsignals as well as inter-symbol interference from all of the transmittedsignals. The successive cancellation receiver processing technique canbe extended to handle dispersive MIMO channel. To deal with crosstalkand inter-symbol interference, the spatial processing in a narrowbandreceiver (which handles crosstalk well but does not effectively dealwith inter-symbol interference) may be replaced with space-timeprocessing in the wideband receiver. In the wideband receiver, thesuccessive cancellation receiver processing technique may be employed insimilar manner as that described above for FIG. 4. However, the spatialprocessing performed in step 412 is replaced with space-time processing.

In one embodiment, a MMSE linear equalizer (MMSE-LE) may be used for thespace-time processing in a wideband receiver. Using the MMSE-LEtechnique, the space-time processing assumes similar form as the spatialprocessing for the narrowband channel. However, each “filter tap” in thespatial processor includes more than one tap, as described in furtherdetail below. The MMSE-LE technique is most effective for use inspace-time processing when the channel estimates (i.e., the channelcoefficient matrix H) are accurate.

In another embodiment, a decision feedback equalizer (DFE) may be usedfor the space-time processing at the wideband receiver. The DFE is anon-linear equalizer that is effective for channels with severeamplitude distortion and uses decision feedback to cancel interferencefrom symbols that have already been detected. If the data stream can bedecoded without errors (or with minimal errors), then the inter-symbolinterference generated by the modulation symbols corresponding to thedecoded data bits may be effectively canceled.

In yet another embodiment, a maximum-likelihood sequence estimator(MLSE) may be used for the space-time processing.

The DFE and MLSE techniques may reduce or possibly eliminate thedegradation in performance when channel estimates are not as accurate.The DFE and MLSE techniques are described in further detail by S. L.Ariyavistakul et al. in a paper entitled “Optimum Space-Time Processorswith Dispersive Interference: Unified Analysis and Required FilterSpan,” IEEE Trans. on Communication, Vol. 7, No. 7, July 1999, andincorporated herein by reference.

Adaptive transmitter processing based on the available CSI andsuccessive cancellation receiver processing may also be advantageouslyemployed for dispersive MIMO channels. The SNR for a recoveredtransmitted signal from the output of each space-time processing stagemay comprise the CSI for that transmitted signal. This information maybe fed back to the transmitter to aid in the selection of an appropriatecoding and modulation scheme for the data stream associated with thattransmitted signal.

Receiver Structure

FIG. 5 is a block diagram of a receiver system 150 a capable ofimplementing various aspects and embodiments of the invention. Receiversystem 150 a implements the successive cancellation receiver processingtechnique to receive and recover the transmitted signals. Thetransmitted signals from (up to) N_(T) transmit antennas are received byeach of N_(R) antennas 152 a through 152 r and routed to a respectivedemodulator (DEMOD) 154 (which is also referred to as a front-endprocessor). For example, receive antenna 152 a may receive a number oftransmitted signals from a number of transmit antennas, and receiveantenna 152 r may similarly receive multiple transmitted signals. Eachdemodulator 154 conditions (e.g., filters and amplifies) a respectivereceived signal, downconverts the conditioned signal to an intermediatefrequency or baseband, and digitizes the downconverted signal to providesamples. Each demodulator 154 may further demodulate the samples with areceived pilot to generate a stream of received modulation symbols,which is provided to RX MIMO/data processor 156.

If OFDM is employed for the data transmission, each demodulator 154further performs processing complementary to that performed by modulator122 shown in FIG. 3. In this case, each demodulator 154 includes an FFTprocessor (not shown) that generates transformed representations of thesamples and provides a stream of modulation symbol vectors. Each vectorincludes N_(L) modulation symbols for N_(L) frequency subchannels andone vector is provided for each time slot. The modulation symbol vectorstreams from the FFT processors of all N_(R) demodulators are thenprovided to a demultiplexer (not shown in FIG. 5), which “channelizes”the modulation symbol vector stream from each FFT processor into anumber of (up to N_(L)) modulation symbol streams. For the transmitprocessing scheme in which each frequency subchannel is independentlyprocessed (e.g., as shown in FIG. 3), the demultiplexer further provideseach of (up to) N_(L) modulation symbol streams to a respective RXMIMO/data processor 156.

For a MIMO system utilizing OFDM, one RX MIMO/data processor 156 may beused to process the N_(R) modulation symbol streams from the N_(R)received antennas for each of the N_(L) frequency subchannels used fordata transmission. And for a MIMO system not utilizing OFDM, one RXMIMO/data processor 156 may be used to process the N_(R) modulationsymbol streams from the N_(R) received antennas.

In the embodiment shown in FIG. 5, RX MIMO/data processor 156 includes anumber of successive (i.e., cascaded) receiver processing stages 510,one stage for each of the transmission channels used for datatransmission. In one transmit processing scheme, one data stream istransmitted on each transmission channel, and each data stream isindependently processed (e.g., with its own encoding and modulationscheme) and transmitted from a respective transmit antenna. For thistransmit processing scheme, the number of data streams is equal to thenumber of transmission channels, which is equal to the number oftransmit antennas used for data transmission (which may be a subset ofthe available transmit antennas). For clarity, RX MIMO/data processor156 is described for this transmit processing scheme.

Each receiver processing stage 510 (except for the last stage 510 n)includes a channel MIMO/data processor 520 coupled to an interferencecanceller 530, and the last stage 510 n includes only channel MIMO/dataprocessor 520 n. For the first receiver processing stage 510 a, channelMIMO/data processor 520 a receives and processes the N_(R) modulationsymbol streams from demodulators 154 a through 154 r to provide adecoded data stream for the first transmission channel (or the firsttransmitted signal). And for each of the second through last stages 510b through 510 n, channel MIMO/data processor 520 for that stage receivesand processes the N_(R) modified symbol streams from the interferencecanceller in the preceding stage to derive a decoded data stream for thetransmission channel being processed by that stage. Each channelMIMO/data processor 520 further provides CSI (e.g., the SNR) for theassociated transmission channel.

For the first receiver processing stage 510 a, interference canceller530 a receives the N_(R) modulation symbol streams from all N_(R)demodulators 154. And for each of the second through second-to-laststages, interference canceller 530 receives the N_(R) modified symbolstreams from the interference canceller in the preceding stage. Eachinterference canceller 530 also receives the decoded data stream fromchannel MIMO/data processor 520 within the same stage, and performs theprocessing (e.g., encoding, interleaving, modulation, channel response,and so on) to derive N_(R) remodulated symbol streams that are estimatesof the interference components of the received modulation symbol streamsdue to this decoded data stream. The remodulated symbol streams are thensubtracted from the received modulation symbol streams to derive N_(R)modified symbol streams that include all but the subtracted (i.e.,cancelled) interference components. The N_(R) modified symbol streamsare then provided to the next stage.

In FIG. 5, a controller 540 is shown coupled to RX MIMO/data processor156 and may be used to direct various steps in the successivecancellation receiver processing performed by processor 156.

FIG. 5 shows a receiver structure that may be used in a straightforwardmanner when each data stream is transmitted over a respective transmitantenna (i.e., one data stream corresponding to each transmittedsignal). In this case, each receiver processing stage 510 may beoperated to recover one of the transmitted signals and provide thedecoded data stream corresponding to the recovered transmitted signal.

For some other transmit processing schemes, a data stream may betransmitted over multiple transmit antennas, frequency subchannels,and/or time intervals to provide spatial, frequency, and time diversity,respectively. For these schemes, the receiver processing initiallyderives a received modulation symbol stream for the transmitted signalon each transmit antenna of each frequency subchannel. Modulationsymbols for multiple transmit antennas, frequency subchannels, and/ortime intervals may be combined in a complementary manner as thedemultiplexing performed at the transmitter system. The stream ofcombined modulation symbols is then processed to provide the associateddecoded data stream.

Spatial Processing Techniques for Non-Dispersive Channels

As noted above, a number of linear spatial processing techniques may beused to process the signals received via a non-dispersive channel torecover each transmitted signal stream from interference caused by theother transmitted signal streams. These techniques include the CCMI,MMSE, and possibly other techniques. The linear spatial processing isperformed within each channel MIMO/data processor 520 on the N_(R) inputsignals. For the first receiver processing stage 510 a, the inputsignals are the N_(R) received signals from the N_(R) received antennas.And for each subsequent stage, the input signals are the N_(R) modifiedsignals from the interference canceller from the preceding stage, asdescribed above. For clarity, the CCMI and MMSE techniques are describedfor the first stage. However, the processing for each subsequent stageproceeds in similar manner with the proper substitution for the inputsignals. More specifically, at each subsequent stage the signalsdetected in the previous stage are assumed to be cancelled, so thedimensionality of the channel coefficient matrix is reduced at eachstage as described below.

In a MIMO system with N_(T) transmit antennas and N_(R) receiveantennas, the received signals at the output of the N_(R) receiveantennas may be expressed as:r=Hx+n, Eq (6)where r is the received symbol vector (i.e., the N_(R)x1 vector outputfrom the MIMO channel, as derived from the receive antennas), H is thechannel coefficient matrix, x is the transmitted symbol vector (i.e.,the N_(T)x1 vector input into the MIMO channel), and n is an N_(R)X1vector representing noise plus interference. The received symbol vectorr includes N_(R) modulation symbols from N_(R) signals received viaN_(R) receive antennas at a specific time slot. Similarly, thetransmitted symbol vector x includes N_(T) modulation symbols in N_(T)signals transmitted via N_(T) transmit antennas at a specific time slot.The channel coefficient matrix H can be further written as:H=μ[h ₁ h ₂. . . h _(N) _(T) ]  Eq (6 a)where the vectors h _(i) contain the channel coefficients associatedwith the i-th transmit antenna. At each subsequent step in thesuccessive cancellation process, the column vectors in equation (6a)associated with previously cancelled signals are removed. Assuming forsimplicity that the transmit signals are cancelled in the same orderthat the associated channel coefficient vectors are listed in equation(6a), then at the k-th stage in the successive cancellation process, thechannel coefficient matrix is:H=[h _(k) h _(k+1) h _(N) _(T) ]  (6 b)

CCMI Technique

For the CCMI spatial processing technique, the receiver system firstperforms a channel matched filter operation on the received symbolvector r. The matched-filtered output can be expressed as:H ^(H) r=H ^(H) Hx+HFH n,  Eq (7)where the superscript ^(“H”) represents transpose and complex conjugate.A square matrix R may be used to denote the product of the channelcoefficient matrix H with its conjugate-transpose H ^(H) (i.e., R=H ^(H)H).

The channel coefficient matrix H may be derived, for example, from pilotsymbols transmitted along with the data. In order to perform “optimal”reception and to estimate the SNR of the transmission channels, it isoften convenient to insert some known symbols into the transmit datastream and to transmit the known symbols over one or more transmissionchannels. Such known symbols are also referred to as pilot symbols orpilot signals. Methods for estimating a single transmission channelbased on a pilot signal and/or a data transmission may be found in anumber of papers available in the art. One such channel estimationmethod is described by F. Ling in a paper entitled “Optimal Reception,Performance Bound, and Cutoff-Rate Analysis of References-AssistedCoherent CDMA Communications with Applications,” IEEE Transaction onCommunication, October 1999. This or some other channel estimationmethod may be extended to matrix form to derive the channel coefficientmatrix H, as is known in the art.

An estimate of the transmitted symbol vector, x′, may be obtained bymultiplying the matched-filtered vector H ^(H) r with the inverse (orpseudo-inverse) of R, which can be expressed as: $\begin{matrix}\begin{matrix}{{\underset{\_}{x}}^{\prime} = {{\underset{\_}{R}}^{- 1}{\underset{\_}{H}}^{H}\underset{\_}{r}}} \\{= {\underset{\_}{x} + {{\underset{\_}{R}}^{- 1}{\underset{\_}{H}}^{H}\underset{\_}{n}}}} \\{= {\underset{\_}{x} + {{\underset{\_}{n}}^{\prime}.}}}\end{matrix} & {{Eq}\quad(8)}\end{matrix}$From the above equation, it can be observed that the transmitted symbolvector x may be recovered by matched filtering (i.e., multiplying withthe matrix H^(H)) the received symbol vector r and then multiplying thefiltered result with the inverse square matrix R⁻¹.

For the CCMI technique, the SNR of the received symbol vector afterprocessing (i.e., the i-th element of x′) can be expressed as:$\begin{matrix}{{SNR}_{i} = {\frac{\overset{\_}{{x_{i}^{\prime}}^{2}}}{\sigma_{n^{\prime}}^{2}}.}} & {{Eq}\quad(9)}\end{matrix}$If the variance of the i-th transmitted symbol {overscore (|x′_(i)|²)}is equal to one (1.0) on the average, the SNR of the receive symbolvector after processing may be expressed as:${SNR}_{i} = {\frac{1}{{\overset{\Cup}{r}}_{ii}\sigma_{n}^{2}}.}$The noise variance may be normalized by scaling the i-th element of thereceived symbol vector by 1{square root}{square root over (r)}_(ii).

If a modulation symbol stream was duplicated and transmitted overmultiple transmit antennas, then these modulation symbols may be summedtogether to form combined modulation symbols. For example, if a datastream was transmitted from all antennas, then the modulation symbolscorresponding to all N_(T) transmit antennas are summed, and thecombined modulation symbol may be expressed as: $\begin{matrix}{x_{total}^{\prime} = {\sum\limits_{i = 1}^{N_{T}}\quad{\frac{x_{i}^{\prime}}{{\overset{\Cup}{r}}_{ii}}.}}} & {{Eq}\quad(10)}\end{matrix}$Alternatively, the transmitter may be operated to transmit one or moredata streams on a number of transmission channels using the same codingand modulation scheme on some or all transmit antennas. In this case,only one SNR (e.g., an average SNR) may be needed for the transmissionchannels for which the common coding and modulation scheme is applied.For example, if the same coding and modulation scheme is applied on alltransmit antennas, then the SNR of the combined modulation symbol,SNR_(total), can be derived. This SNR_(total) would then have a maximalcombined SNR that is equal to the sum of the SNR of the modulationsymbols from the N_(T) transmit antennas. The combined SNR may beexpressed as: $\begin{matrix}{{SNR}_{total} = {{\sum\limits_{i = 1}^{N_{T}}\quad{SNR}_{i}} = {\frac{1}{\sigma_{n}^{2}}{\sum\limits_{i = 1}^{N_{T}}{\frac{1}{{\overset{\Cup}{r}}_{ii}}.}}}}} & {{Eq}\quad(11)}\end{matrix}$

FIG. 6A is a block diagram of an embodiment of a channel MIMO/dataprocessor 520 x, which is capable of implementing the CCMI techniquedescribed above. Channel MIMO/data processor 520 x includes a processor610 x (which performs CCMI processing) coupled to a RX data processor620.

Within processor 610 x, the received modulation symbol vectors r arefiltered by a match filter 614, which pre-multiplies each vector r withthe conjugate-transpose channel coefficient matrix H^(H), as shown abovein equation (7). The channel coefficient matrix H may be estimated basedon pilot signals in a manner similar to that used for conventional pilotassisted single and multi-carrier systems, as is known in the art. Thematrix R is computed according to the equation R=H ^(H) H, as shownabove. The filtered vectors are further pre-multiplied by a multiplier616 with the inverse square matrix R⁻¹ to form an estimate x′ of thetransmitted modulation symbol vector x, as shown above in equation (8).

For certain transmit processing schemes, the estimated modulation symbolstreams corresponding to multiple transmit antennas used for thetransmission of a data stream may be provided to a combiner 618, whichcombines redundant information across time, space, and frequency. Thecombined modulation symbols x″ are then provided to RX data processor620. For some other transmit processing schemes, the estimatedmodulation symbols x′ may be provided directly (not shown in FIG. 6A) toRX data processor 620.

Processor 610 x thus generates a number of independent symbol streamscorresponding to the number of data streams transmitted from thetransmitter system. Each symbol stream includes recovered modulationsymbols that correspond to and are estimates of the modulation symbolsafter the symbol mapping at the transmitter system. The (recovered)symbol streams are then provided to RX data processor 620.

As noted above, each stage 510 within RX MIMO/data processor 156recovers and decodes one of the transmitted signals (e.g., thetransmitted signal with the best SNR) included in that stage's inputsignals. The estimation of the SNRs for the transmitted signals isperformed by a CSI processor 626, and may be achieved based on equations(9) and (11) described above. CSI processor 626 then provides the CSI(e.g., SNR) for the transmitted signal that has been selected (e.g., the“best”) for recovery and decoding, and further provides a control signalidentifying the selected transmitted signal.

FIG. 7 is a block diagram of an embodiment of RX data processor 620. Inthis embodiment, a selector 710 within RX data processor 620 receives anumber of symbol streams from a preceding linear spatial processor andextracts the symbol stream corresponding to the selected transmittedsignal, as indicated by the control signal from CSI processor 626. In analternative embodiment, RX data processor 620 is provided with thesymbol stream corresponding to the selected transmitted signal and thestream extraction may be performed by combiner 618 based on the controlsignal from CSI processor 626. In any case, the extracted stream ofmodulation symbols is provided to a demodulation element 712.

For the transmitter embodiment shown in FIG. 2 in which the data streamfor each transmission channel is independently coded and modulated basedon the channel's SNR, the recovered modulation symbols for the selectedtransmission channel are demodulated in accordance with a demodulationscheme (e.g., M-PSK, M-QAM) that is complementary to the modulationscheme used for the transmission channel. The demodulated data fromdemodulation element 712 is then deinterleaved by a deinterleaver 714 ina complementary manner to that performed by channel interleaver 214, andthe de-interleaved data is further decoded by a decoder 716 in acomplementary manner to that performed by encoder 212. For example, aTurbo decoder or a Viterbi decoder may be used for decoder 716 if Turboor convolutional coding, respectively, is performed at the transmitter.The decoded data stream from decoder 716 represents an estimate of thetransmitted data stream being recovered.

Referring back to FIG. 6A, the estimated modulation symbols x′ and/orthe combined modulation symbols x″ are also provided to CSI processor626, which estimates the SNR for each of the transmission channels. Forexample, CSI processor 626 may estimate the noise covariance matrixφ_(nn) based on the pilot signals received and then compute the SNR ofthe i-th transmission channel based on equation (9) or (11). The SNR canbe estimated similar to conventional pilot assisted single andmulti-carrier systems, as is known in the art. The SNR for all of thetransmission channels may comprise the CSI that is reported back to thetransmitter system for this transmission channel. CSI processor 626further provides to RX data processor 620 or combiner 618 the controlsignal that identifies the selected transmission channel.

The estimated modulation symbols x′ are further provided to a channelestimator 622 and a matrix processor 624 that respectively estimates thechannel coefficient matrix H and derives the square matrix R. Theestimated modulation symbols corresponding to pilot data and/or trafficdata may be used for the estimation of the channel coefficient matrix H.

Referring back to FIG. 5, the input signals to the first stage 510 aincludes all transmitted signals, and the input signals to eachsubsequent stage includes one transmitted signal (i.e., one term)canceled by a preceding stage. Thus, channel MIMO/data processor 520 awithin the first stage 510 a may be designed and operated to estimatethe channel coefficient matrix H and to provide this matrix to allsubsequent stages.

The CSI information to be reported by receiver system 150 back totransmitter system 110 may comprise the SNRs for the transmissionchannels, as determined by the stages within RX MIMO/data processor 156.

MMSE Technique

For the MMSE spatial processing technique, the receiver system firstperforms a multiplication of the received symbol vector r with aweighting coefficient matrix M to derive an initial MMSE estimate{circumflex over (x)} of the transmitted symbol vector x, which can beexpressed as: $\begin{matrix}\begin{matrix}{\hat{\underset{\_}{x}} = {\underset{\_}{M}\quad\underset{\_}{r}}} \\{{= {{{\underset{\_}{H}}^{H}\left( {{\underset{\_}{H}\quad{\underset{\_}{H}}^{H}} + {\underset{\_}{\phi}}_{nn}} \right)}^{- 1}\underset{\_}{r}}}\quad,}\end{matrix} & {{Eq}\quad(12)}\end{matrix}$whereM=H ^(H)( HH ^(H)+φ _(nn))⁻¹.  Eq (13)The matrix M is selected such that the mean square error of the errorvector e between the initial MMSE estimate {circumflex over (x)} and thetransmitted symbol vector x (i.e., e={circumflex over (x)}−x) isminimized.

To determine the SNR of the transmission channels for the MMSEtechnique, the signal component can first be determined based on themean of {circumflex over (x)} given x, averaged over the additive noise,which can be expressed as: $\begin{matrix}{{E\left\lbrack \hat{\underset{\_}{x}} \middle| \underset{\_}{x} \right\rbrack} = {E\left\lbrack {\underset{\_}{M}\quad\underset{\_}{r}} \middle| \underset{\_}{x} \right\rbrack}} \\{= {{{\underset{\_}{H}}^{H}\left( {{\underset{\_}{H}\quad{\underset{\_}{H}}^{H}} + {\underset{\_}{\phi}}_{nn}} \right)}^{- 1}{E\left\lbrack \underset{\_}{r} \right\rbrack}}} \\{= {{{\underset{\_}{H}}^{H}\left( {{\underset{\_}{H}\quad{\underset{\_}{H}}^{H}} + {\underset{\_}{\phi}}_{nn}} \right)}^{- 1}\underset{\_}{H}\quad\underset{\_}{x}}} \\{{= {\underset{\_}{V}\quad\underset{\_}{x}}}\quad,}\end{matrix}$where the matrix V can be expressed as:V=H ^(H)(φ _(nn) +HH ^(H))⁻¹ H=H ^(H) φ _(nn) ⁻¹ H ( I H ^(H) φ _(nn) ⁻¹H )⁻¹.

The i-th element {circumflex over (x)}_(i) of the initial MMSE estimate{circumflex over (x)} can be expressed as:{circumflex over (x)} _(i) =v _(i1) x ₁ +. . . +v _(ii) x _(i) +. . . +v_(iN) _(R) x _(N) _(R) .  Eq (14)If all of the elements of {circumflex over (x)} are uncorrelated andhave zero mean, the expected value of the i-th element of {circumflexover (x)} can be expressed as:E[{circumflex over (x)} _(i) |x]=v _(ii) x _(i).  Eq (15)

As shown in equation (15), {circumflex over (x)} is a biased estimate ofx₁ and this bias can be removed to obtain improved performance. Anunbiased estimate of x_(i) can be obtained by dividing {circumflex over(x)}_(i) by v_(ii). Thus, the unbiased minimum mean square errorestimate of x, {tilde over (x)}, can be obtained by pre-multiplying thebiased estimate {circumflex over (x)} by a diagonal matrix D _(v) ⁻¹, asfollows:{tilde over (x)}=D _(v) ⁻ {circumflex over (x)},  Eq (16)whereD _(v) ⁻¹ diag(1/v ₁₁,1/v ₂₂, . . . ,1/v _(N) _(R) _(N) _(R) ),  Eq (17)and v_(ii) are the diagonal elements of the matrix V.

To determine the noise plus interference, the error ê between theunbiased estimate {tilde over (x)} and the transmitted symbol vector xcan be expressed as: $\begin{matrix}{\underset{\_}{\hat{e}} = {\underset{\_}{x} - {{\underset{\_}{D}}_{v}^{- 1}\underset{\_}{\hat{x}}}}} \\{= {\underset{\_}{x} - {{\underset{\_}{D}}_{v}^{- 1}{{\underset{\_}{H}}^{H}\left( {{\underset{\_}{H}\quad{\underset{\_}{H}}^{H}} + {\underset{\_}{\phi}}_{nn}} \right)}^{- 1}{\underset{\_}{r}.}}}}\end{matrix}$

For the MMSE technique, the SNR of the received symbol vector afterprocessing (i.e., the i-th element of {tilde over (x)}) can be expressedas: $\begin{matrix}{{SNR}_{i} = {\frac{E\left\lbrack \overset{\_}{{x_{i}}^{2}} \right\rbrack}{u_{ii}}.}} & {{Eq}\quad(18)}\end{matrix}$where u_(ii) is the variance of the i-th element of the error vector ê,and the matrix U can be expressed as:U=I−D _(v) ⁻¹ V−VD _(v) ⁻¹ +VD _(v) ⁻¹ +D _(v) ⁻¹ VD _(v) ⁻¹.  Eq (19)If the variance, {overscore (|x_(i)|²)}, of the i-th transmitted symbol,x_(i), is equal to one (1.0) on the average, and from equation (19)${u_{ii} = {\frac{1}{v_{ii}} - 1}},$then the SNR of the received symbol vector after processing may beexpressed as: $\begin{matrix}{{SNR}_{i} = {\frac{v_{ii}}{1 - v_{ii}}.}} & {{Eq}\quad(20)}\end{matrix}$The estimated modulation symbols, {tilde over (x)}, may similarly becombined to obtain combined modulation symbols, as described above forthe CCMI technique.

FIG. 6B is a block diagram of an embodiment of a channel MIMO/dataprocessor 520 y, which is capable of implementing the MMSE techniquedescribed above. Channel MIMO/data processor 520 y includes a processor610 y (which performs MMSE processing) coupled to RX data processor 620.

Within processor 610 y, the received modulation symbol vectors r arepre-multiplied with the matrix M by a multiplier 634 to form an estimate{circumflex over (x)} of the transmitted symbol vector x, as shown abovein equation (8). Similar to the CCMI technique, the matrices H and φ_(nn) may be estimated based on the received pilot signals and/or datatransmissions. The matrix M is then computed according to equation (9).The estimate {circumflex over (x)} is further pre-multiplied with thediagonal matrix D _(v) ⁻¹ by a multiplier 636 to form an unbiasedestimate {tilde over (x)} of the transmitted symbol vector x, as shownabove in equation (12).

Again, for certain transmit processing schemes, a number of streams ofestimated modulation symbols {tilde over (x)} corresponding to a numberof transmit antennas used for transmitting a data stream may be providedto a combiner 638, which combines redundant information across time,space, and frequency. The combined modulation symbols {tilde over (x)}″are then provided to RX data processor 620. For some other transmitprocessing schemes, the estimated modulation symbols {tilde over (x)}may be provided directly (not shown in FIG. 6B) to RX data processor620. RX data processor 620 demodulates, de-interleaves, and decodes themodulation symbol stream corresponding to the data stream beingrecovered, as described above.

The estimated modulation symbols {tilde over (x)} and/or the combinedmodulation symbols {tilde over (x)}″ are also provided to CSI processor626, which estimates the SNR for each of the transmitted signals. Forexample, CSI processor 626 may estimate the SNR of the i-th transmittedsignal based on equation (18) or (20). The SNR for the selectedtransmitted signal may be reported back to the transmitter system. CSIprocessor 626 further provides to RX data processor 620 or combiner 618the control signal that identifies the selected transmitted signal.

The estimated modulation symbols {tilde over (x)} are further providedto an adaptive processor 642 that derives the matrix M and the diagonalmatrix D _(v) ⁻¹ based on equations (13) and (17), respectively.

Space-Time Processing Techniques for Time-Dispersive Channels

As noted above, a number of space-time processing techniques may be usedto process the signals received via a time-dispersive channel. Thesetechniques include the use of time domain channel equalizationtechniques such as MMSE-LE, DFE, MLSE, and possibly other techniques, inconjunction with the spatial processing techniques described above for anon-dispersive channel. The space-time processing is performed withineach channel MIMO/data processor 520 on the N_(R) input signals.

MMSE-LE Technique

In the presence of time dispersion, the channel coefficient matrix Htakes on a delay dimension, and each element of the matrix H behaves asa linear transfer function instead of a coefficient. In this case, thechannel coefficient matrix H can be written in the form of a channeltransfer function matrix H(τ), which can be expressed as:H (τ)={h _(ij)(τ)} for 1≦i≦N_(R), and 1≦j≦N_(T).  Eq (21)where h_(ij)(τ) is the linear transfer function from the j-th transmitantenna to the i-th receive antenna. As a result of the linear transferfunctions h_(ij)(τ), the received signal vector r(t) is a convolution ofthe channel transfer function matrix H(τ) with the transmitted signalvector x(t), which can be expressed as:r (t)=∫ H (τ)x(t−τ)dτ.  Eq (22)

As part of the demodulation function (performed by demodulators 154 inFIG. 5), the received signals are sampled to provide received samples.Without loss of generality, the time-dispersive channel and the receivedsignals can be represented in a discrete-time representation in thefollowing description. First, the channel transfer function vector h_(j)(k) associated with the j-th transmit antenna at delay k can beexpressed as:h _(j)(k)=[h _(ij)(k)h _(2j)(k) . . . h _(N) _(R) _(j)(k)]^(T) for0≦k≦L,  Eq (23)where h_(ij)(k) is the k-th tap weight of the channel transfer functionassociated with the path between the j-th transmit antenna and the i-threceive antenna and L is the maximum extent (in sample intervals) of thechannel time dispersion. Next, the N_(R)×N_(T) channel transfer functionmatrix at delay k can be expressed as:H (k)=[ h ₁(k) h ₂(k) . . . h _(N) _(T) (k)] for 0≦k≦L.  Eq (24)

The received signal vector r(n) at sample time n can then be expressedas: $\begin{matrix}{{{\underset{\_}{r}(n)} = {{{\sum\limits_{k = 0}^{L}\quad{{\underset{\_}{H}(k)}{\underset{\_}{x}\left( {n - k} \right)}}} + {\underset{\_}{n}(n)}} = {{\underset{\underset{\_}{\_}}{H}\quad{\underset{\underset{\_}{\_}}{x}(n)}} + {\underset{\_}{n}(n)}}}},} & {{Eq}\quad(25)}\end{matrix}$where H is an N_(R)×(L+1)N_(T) block-structured matrix that representsthe sampled channel matrix transfer function, H(k), and can berepresented as:H=[H(0) H (1) . . . H (L)],and x(n) is a sequence of L+1 vectors of received samples captured forL+1 sample intervals, with each vector comprising N_(R) samples for theN_(R) received antennas, and can be represented as:${\underset{\underset{\_}{\_}}{x}(n)} = {\begin{bmatrix}{\underset{\_}{x}(n)} \\{\underset{\_}{x}\left( {n - 1} \right)} \\\vdots \\{\underset{\_}{x}\left( {n - L} \right)}\end{bmatrix}.}$

An MMSE linear space-time processor computes an estimate of thetransmitted symbol vector, {circumflex over (x)}(n), at time n byperforming a convolution of the sequence of received signal vectors r(n)with the sequence of 2K+1, N_(R)×N_(T) weight matrices M(k), as follows:$\begin{matrix}{{{\underset{\_}{\hat{x}}(n)} = {{\sum\limits_{k = {- K}}^{K}\quad{{\underset{\_}{M}(k)}{\underset{\_}{r}\left( {n - k} \right)}}} = {\underset{\underset{\_}{\_}}{M}\quad{\underset{\underset{\_}{\_}}{r}(n)}}}},} & {{Eq}\quad(26)}\end{matrix}$where M=[M(−K) . . . M(0) . . . M(K)], K is a parameter that determinesthe delay-extent of the equalizer filter, and${\underset{\underset{\_}{\_}}{r}(n)} = {\begin{bmatrix}{\underset{\_}{r}\left( {n + K} \right)} \\\vdots \\{\underset{\_}{r}(n)} \\\vdots \\{\underset{\_}{r}\left( {n - K} \right)}\end{bmatrix}.}$The sequence of weight matrices M(k) is selected to minimize themean-square error, which can be expressed as:ε=E{e ^(H)(k) e (k)},  Eq (27)where the error e(k) can be expressed as:e (k)= {circumflex over (x)} (k)− x (k).  Eq (28)

The MMSE solution can then be stated as the sequence of weight matricesM(k) that satisfy the linear constraints: $\begin{matrix}{{\sum\limits_{k = {- K}}^{K}\quad{{\underset{\_}{M}(k)}{\underset{\_}{R}\left( {k - l} \right)}}} = \left\{ {\begin{matrix}{0,} & {{- K} \leq l < {- L}} \\{{{\underset{\_}{H}}^{H}\left( {- l} \right)},} & {{- L} \leq l \leq 0} \\{0,} & {0 < l \leq K}\end{matrix},} \right.} & {{Eq}\quad(29)}\end{matrix}$where R(k) is a sequence of N_(R)×N_(R) space-time correlation matrices,which can be expressed as: $\begin{matrix}{{\underset{\_}{R}(k)} = {{E\left\{ {{\underset{\_}{r}\left( {n - k} \right)}{{\underset{\_}{r}}^{H}(n)}} \right\}} = \left\{ \begin{matrix}{{{\sum\limits_{m = {\max{({0,{- k}})}}}^{\min{({L,{L - k}})}}\quad{{\underset{\_}{H}(m)}{{\underset{\_}{H}}^{H}\left( {m + k} \right)}}} + {{\underset{\_}{\varphi}}_{zz}(k)}},} & {{- L} \leq k \leq L} \\{{{\underset{\_}{\varphi}}_{zz}(k)},} & {otherwise}\end{matrix} \right.}} & {{Eq}\quad(30)}\end{matrix}$where φ _(zz)(k) is the noise autocorrelation function, which can beexpressed as:φ _(zz)(k)=E{z (l−k) z ^(H)(l)}.  Eq (31)For white (temporally uncorrelated) noise, φ _(zz)(k)=φ _(zz)δ(k), whereφ _(zz) in this case represents only the spatial correlation matrix. Forspatially and temporally uncorrelated noise with equal power at eachreceive antenna, φ(k)=σ² ∥δ(k).

Equation (29) can further be represented as:MR={tilde over (H)} ^(H), or M={tilde over (H)} ^(H) R ⁻¹,  Eq (32)where R is block-Toeplitz with block j,k given by R(j−k) and${\overset{\sim}{\underset{\underset{\_}{\_}}{H}} = \begin{bmatrix}{\underset{\_}{0}}_{{({K - L})}N_{R} \times N_{T}} \\{\underset{\_}{H}(L)} \\{\underset{\_}{H}\left( {L - 1} \right)} \\\vdots \\{\underset{\_}{H}(0)} \\{\underset{\_}{0}}_{K,{N_{R} \times N_{T}}}\end{bmatrix}},$where 0 _(m×n) is an m×n matrix of zeros.

As with the MMSE spatial processing described above, to determine theSNR associated with the symbol estimates, an unbiased minimum meansquare error estimate is derived. First, for the MMSE-LE estimatederived above, $\begin{matrix}\begin{matrix}{{E\left\lbrack {\underset{\_}{\hat{x}}(n)} \middle| {\underset{\_}{x}(n)} \right\rbrack} = {\underset{\underset{\_}{\_}}{M}\quad{E\left\lbrack {\underset{\underset{\_}{\_}}{r}(n)} \middle| {\underset{\_}{x}(n)} \right\rbrack}}} \\{= \left\lbrack {{{\underset{\_}{M}\left( {- K} \right)}\underset{\underset{\_}{\_}}{H}{\underset{\underset{\_}{\_}}{x}\left( {n + K} \right)}} + \cdots + {{\underset{\_}{M}(0)}\underset{\underset{\_}{\_}}{H}{\underset{\underset{\_}{\_}}{x}(n)}} + \cdots +} \right.} \\{\left. {{\underset{\_}{M}(K)}{\underset{\underset{\_}{\_}}{Hx}\left( {n - K} \right)}} \right\rbrack,}\end{matrix} & {{Eq}\quad(33)}\end{matrix}$where the expectation is taken over the noise. If it is assumed that themodulation symbols are uncorrelated in time and the expectation is takenover all intersymbol interference in the above (all transmitted signalcomponents not transmitted at time n), then the expectation can beexpressed as: $\begin{matrix}\begin{matrix}{{E\left\lbrack {\underset{\_}{\hat{x}}(n)} \middle| {\underset{\_}{x}(n)} \right\rbrack} = {\underset{\underset{\_}{\_}}{M}\quad{E\left\lbrack {\underset{\underset{\_}{\_}}{r}(n)} \middle| {\underset{\_}{x}(n)} \right\rbrack}}} \\{= \left\lbrack {{{\underset{\_}{M}(0)}{\underset{\_}{H}(0)}} + {{\underset{\_}{M}\left( {- 1} \right)}{\underset{\_}{H}(1)}} + \cdots +} \right.} \\{\left. {{\underset{\_}{M}\left( {- L} \right)}{\underset{\_}{H}(L)}} \right\rbrack{\underset{\_}{x}(n)}} \\{= {\underset{\underset{\_}{\_}}{M}\quad\underset{\underset{\_}{\_}}{\overset{\sim}{H}}{\underset{\_}{x}(n)}}} \\{= {\underset{\_}{V}{\underset{\_}{x}(n)}}}\end{matrix} & {{Eq}\quad(34)}\end{matrix}$where

-   -   V=M{tilde over (H)}={tilde over (H)} ^(H) R ⁻¹ {tilde over (H)}.

Finally, after averaging over the interference from other spatialsubchannels, the average value of the signal from the i-th transmitantenna at time n can be expressed as:E[{tilde over (x)} _(i)(n)|x _(i)(n)]=v _(ii) x _(i)(n),  Eq (35)where v_(ii) is the i-th diagonal element of V (v_(ii) is a scalar), and{circumflex over (x)}_(i)(n) is the i-th element of the MMSE-LEestimate.

By definingD _(v) ⁻¹ =diag(1/v ₁₁,1/v ₂₂, . . . 1/v _(N) _(T) _(N) _(T) ),  Eq (36)then the unbiased MMSE-LE estimate of the transmitted signal vector attime n can be expressed as:{tilde over (x)} (n)= D _(v) ⁻¹ {circumflex over (x)} (n)= D _(v) ⁻¹ Mr(n).  Eq (37)The error covariance matrix associated with the unbiased MMSE-LE can beexpressed as: $\begin{matrix}\begin{matrix}{{\underset{\_}{\varphi}}_{ee} = \underset{\_}{U}} \\{= {E\left\{ {\left\lbrack {{\underset{\_}{x}(n)} - {{\underset{\_}{D}}_{v}^{- 1}\underset{\underset{\_}{\_}}{M}\quad{\underset{\underset{\_}{\_}}{r}(n)}}} \right\rbrack\left\lbrack {{\underset{\_}{x}(n)} - {{{\underset{\underset{\_}{\_}}{r}}^{H}(n)}{\underset{\underset{\_}{\_}}{M}}^{H}{\underset{\_}{D}}_{v}^{- 1}}} \right\rbrack} \right\}}} \\{= {I - {{\underset{\_}{D}}_{v}^{- 1}\underset{\_}{V}} - {\underset{\_}{V}\quad{\underset{\_}{D}}_{v}^{- 1}} + {{\underset{\_}{D}}_{v}^{- 1}\underset{\_}{V}\quad{{\underset{\_}{D}}_{v}^{- 1}.}}}}\end{matrix} & {{Eq}\quad(38)}\end{matrix}$The SNR associated with the estimate of the symbol transmitted on thei-th transmit antenna can finally be expressed as: $\begin{matrix}{{SNR}_{i} = {\frac{1}{u_{ii}} = {\frac{v_{ii}}{1 - v_{ii}}.}}} & {{Eq}\quad(39)}\end{matrix}$

The MMSE-LE technique can be implemented by channel MIMO/data processor520 y in FIG. 6B. In this case, multiplier 634 can be designed toperform the convolution of the sequence of received signal vectors r(n)with the sequence of weight matrices M(k), as shown in equation (26).Multiplier 636 can be designed to perform the pre-multiply of theestimate {circumflex over (x)} with the diagonal matrix D _(v) ⁻¹ toderive the unbiased MMSE-LE estimate {tilde over (x)}, as shown inequation (37). Adaptive processor 642 can be designed to derive thesequence of weight matrices M(k) as shown in equation (32) and thediagonal matrix D _(v) ⁻¹ as shown in equation (36). The subsequentprocessing may be achieved in similar manner as that described above forthe MMSE technique. The SNR of the symbol stream transmitted from thej-th transmit antenna may be estimated based on equation (39) by CSIprocessor 626.

DFE Technique

FIG. 6C is a block diagram of an embodiment of a channel MIMO/dataprocessor 520 z, which is capable of implementing the DFE spatial-timeequalization technique. Channel MIMO/data processor 520 z includes aspace-time processor 610 z, which performs DFE processing, coupled to RXdata processor 620.

For the DFE technique, the received modulation symbol vectors r(n) arereceived and processed by a forward receive processor 654 to provideestimated modulation symbols for the data stream to be recovered.Forward receive processor 654 may implement the CCMI or MMSE techniquedescribed above or some other linear spatial equalization technique. Asummer 656 then combines an estimated distortion components provided bya feedback processor 658 with the estimated modulation symbols toprovide “equalized” modulation symbols having the distortion componentremoved. Initially, the estimated distortion components are zero and theequalized modulation symbols are simply the estimated modulationsymbols. The equalized modulation symbols from summer 656 are thendemodulated and decoded by RX data processor 620 to provide the decodeddata stream.

The decoded data stream is then re-encoded and re-modulated by a channeldata processor 210 x to provide remodulated symbols, which are estimatesof the modulation symbols at the transmitter. Channel data processor 210x performs the same processing (e.g., encoding, interleaving, andmodulation) as that performed at the transmitter for the data stream,e.g., as shown in FIG. 2. The remodulated symbols from channel dataprocessor 210 x are provided to feedback processor 658, which processesthe symbols to derive the estimated distortion components. Feedbackprocessor 658 may implement a linear spatial equalizer (e.g., a lineartransversal equalizer).

The resulting estimate of the transmitted symbol vector at time n can beexpressed as: $\begin{matrix}{{{\underset{\_}{\hat{x}}(n)} = {{\sum\limits_{k = {- K_{1}}}^{0}\quad{{{\underset{\_}{M}}_{f}(k)}{\underset{\_}{r}\left( {n - k} \right)}}} + {\sum\limits_{k = 1}^{K_{2}}\quad{{{\underset{\_}{M}}_{b}(k)}{\underset{\_}{\overset{\sim}{x}}\left( {n - k} \right)}}}}},} & {{Eq}\quad(40)}\end{matrix}$where r(n) is the vector of received modulation symbols, which is givenabove in equation (25), {tilde over (x)}(n) is the vector of symboldecisions provided by the channel data processor 210 x, M _(f)(k),−K₁≦k≦0 is the sequence of (K₁+1)−(N_(T)×N_(R)) feed-forward coefficientmatrices used by forward receive processor 654, and M _(b)(k), 1≦k≦K₂ isthe sequence of K₂−(N_(T)×N_(R)) feed-back coefficient matrices used byfeedback processor 658. Equation (40) can also be expressed as:{tilde over (x)} (n)= M _(f) r (n)+ M _(b) {tilde over (x)} (n),  Eq(41)where M _(f)=[M(−K₁)M(−K₁+1) . . . M(0)], M _(b)=[M(1) M(2) . . .M(K₂)],${{\overset{\sim}{\underset{\underset{\_}{\_}}{x}}(n)} = \begin{bmatrix}{\overset{\sim}{\underset{\_}{x}}\left( {n - 1} \right)} \\{\overset{\sim}{\underset{\_}{x}}\left( {n - 2} \right)} \\\vdots \\{\overset{\sim}{\underset{\_}{x}}\left( {n - K_{2}} \right)}\end{bmatrix}},{{{and}\quad{\underset{\underset{\_}{\_}}{r}(n)}} = {\begin{bmatrix}{\underset{\_}{r}\left( {n + K_{1}} \right)} \\{\underset{\_}{r}\left( {n + K_{1} - 1} \right)} \\\vdots \\{\underset{\_}{r}(n)}\end{bmatrix}.}}$

If the MMSE criterion is used to find the coefficient matrices, then thesolutions for M _(f) and M _(b) that minimize the mean square errorε=E{e ^(H)(k)e(k)} can be used, where the error e(k) is expressed as:e (k)= {circumflex over (x)} (k)− x (k).The MMSE solution for the feed-forward filter can then be expressed as:M _(f) ={tilde over (H)} ^(H) {tilde over (R)} ⁻¹,  Eq (42)where${\overset{\sim}{\underset{\underset{\_}{\_}}{H}} = \begin{bmatrix}{\underset{\_}{0}}_{{({K_{1} - L})}N_{R} \times N_{T}} \\{\underset{\_}{H}(L)} \\{\underset{\_}{H}\left( {L - 1} \right)} \\\vdots \\{\underset{\_}{H}(0)}\end{bmatrix}},$and {tilde over (R)} is a (K₁+1)N_(R)×(K₁+1)N_(R) matrix made up ofN_(R)×N_(R) blocks. The (i,j)-th block in {tilde over (R)} is given by:$\begin{matrix}{{\underset{\_}{\overset{\sim}{R}}\left( {i,j} \right)} = {{\sum\limits_{m = 0}^{K_{1} - i + 1}\quad{{\underset{\_}{H}(m)}{\underset{\_}{H}\left( {m + i - j} \right)}}} + {\sigma^{2}\underset{\_}{I}\quad{{\delta\left( {i - j} \right)}.}}}} & {{Eq}\quad(43)}\end{matrix}$

The MMSE solution for the feed-back filter is: $\begin{matrix}{{{{\underset{\_}{M}}_{b}(k)} = {- {\sum\limits_{j = {- K_{1}}}^{0}{{{\underset{\_}{M}}_{f}(j)}{\underset{\_}{H}\left( {k - j} \right)}}}}},{1 \leq k \leq {K_{2}.}}} & {{Eq}\quad(44)}\end{matrix}$As in the MMSE-LE described above, the unbiased estimate is firstdetermined by finding the conditional mean value of the transmittedsymbol vector:E[{circumflex over (x)} (n)| x (n)]= M _(f) {tilde over (H)} x (n)= V_(dfe) x (n),  Eq (45)where V _(dfe)=M _(f) {tilde over (H)}={tilde over (H)} ^(H) {tilde over(R)} ⁻¹ {tilde over (H)}. Next, the mean value of the i-th element of{circumflex over (x)}(n), {circumflex over (x)}_(i)(n), is expressed as:E[{circumflex over (x)} _(i)(n)|x _(i)(n)]=v _(dfe,ii) x _(i)(n),where v_(dfe,ii) is the i-th diagonal element of V _(dfe). To form theunbiased estimate, similar to that described above, the diagonal matrixwhose elements are the inverse of the diagonal elements of V _(dfe) isfirst defined as:D _(Vdfe) ⁻¹ =diag(v _(dfe,11) ,v _(dfe,22) , . . . ,v _(dfe,N) _(T)_(N) _(T) ).  Eq (46)Then the unbiased estimate is expressed as:{circumflex over ({circumflex over (x)})}( n)= D _(Vdfe) ⁻¹ {circumflexover (x)}( n)= D _(Vdfe) ⁻¹ M _(f) r( n)+D _(Vdfe) ⁻¹ M _(b) {tilde over(x)}( n).  Eq (47)The resulting error covariance matrix is given by: $\begin{matrix}\begin{matrix}{{\underset{\_}{\varphi}}_{ee} = {\underset{\_}{U}}_{dfe}} \\{= {E\left\{ \left\lbrack {{\underset{\_}{x}(n)} - {{\underset{\_}{D}}_{Vdfe}^{- 1}\left( {{{\underset{\underset{\_}{\_}}{M}}_{f}{\underset{\underset{\_}{\_}}{r}(n)}} + {{\underset{\underset{\_}{\_}}{M}}_{b}{\underset{\underset{\_}{\_}}{\overset{\sim}{x}}(n)}}} \right)}} \right\rbrack \right.}} \\\left. \left\lbrack {{{\underset{\_}{x}}^{H}(n)} - {\left( {{{{\underset{\underset{\_}{\_}}{r}}^{H}(n)}{\underset{\underset{\_}{\_}}{M}}_{f}^{H}} + {{{\underset{\underset{\_}{\_}}{\overset{\sim}{x}}}^{H}(n)}{\underset{\underset{\_}{\_}}{M}}_{b}^{H}}} \right){\underset{\_}{D}}_{Vdfe}^{- 1}}} \right\rbrack \right\} \\{= {I - {{\underset{\_}{D}}_{Vdfe}^{- 1}{\underset{\_}{V}}_{dfe}} - {{\underset{\_}{V}}_{dfe}{\underset{\_}{D}}_{Vdfe}^{- 1}} + {{\underset{\_}{D}}_{Vdfe}^{- 1}{\underset{\_}{V}}_{dfe}{\underset{\_}{D}}_{Vdfe}^{- 1}}}}\end{matrix} & {{Eq}\quad(48)}\end{matrix}$The SNR associated with the estimate of the symbol transmitted on thei-th transmit antenna can then be expressed as: $\begin{matrix}{{SNR}_{i} = {\frac{1}{u_{{dfe},{ii}}} = {\frac{v_{{dfe},{ii}}}{1 - v_{{dfe},{ii}}}.}}} & {{Eq}\quad(49)}\end{matrix}$

With the DFE technique, the decoded data stream is used to derive anestimate of the distortion generated by the already decoded informationbits. If the data stream is decoded without errors (or with minimalerrors), then the distortion component can be accurately estimated andthe inter-symbol interference contributed by the already decodedinformation bits may be effectively canceled out. The processingperformed by forward receive processor 654 and feedback processor 658are typically adjusted simultaneously to minimize the mean square error(MSE) of the inter-symbol interference in the equalized modulationsymbols. DFE processing is described in further detail in theaforementioned paper by Ariyavistakul et al.

Interference Cancellation

FIG. 8 is a block diagram of an interference canceller 530 x, which isone embodiment of interference canceller 530 in FIG. 5. Withininterference canceller 530 x, the decoded data stream from the channelMIMO/data processor 520 within the same stage is re-encoded,interleaved, and re-modulated by a channel data processor 210 y toprovide remodulated symbols, which are estimates of the modulationsymbols at the transmitter prior to the MIMO processing and channeldistortion. Channel data processor 210 y performs the same processing(e.g., encoding, interleaving, and modulation) as that performed at thetransmitter for the data stream. The remodulated symbols are thenprovided to a channel simulator 810, which processes the symbols withthe estimated channel response to provide estimates of the interferencedue the decoded data stream.

For a non-dispersive channel, channel simulator 810 multiples theremodulated symbol stream associated with the i-th transmit antenna withthe vector ĥ _(i), which is an estimate of the channel response betweenthe i-th transmit antenna for which the data stream is being recoveredand each of the N_(R) receive antennas. The vector ĥ _(i) may beexpressed as: $\begin{matrix}{{{\hat{\underset{\_}{h}}}_{i} = \begin{bmatrix}\begin{matrix}\begin{matrix}{\hat{h}}_{i,1} \\{\hat{h}}_{i,2}\end{matrix} \\\vdots\end{matrix} \\{\hat{h}}_{i,N_{R}}\end{bmatrix}},} & {{Eq}\quad(50)}\end{matrix}$and is one column of an estimated channel response matrix Ĥ that can beexpressed as: $\begin{matrix}{\underset{\_}{\hat{H}} = {\begin{bmatrix}{\hat{h}}_{1,1} & {\hat{h}}_{2,1} & \cdots & {\hat{h}}_{N_{T},1} \\{\hat{h}}_{1,2} & {\hat{h}}_{2,2} & \cdots & {\hat{h}}_{N_{T},2} \\\vdots & \vdots & ⋰ & \vdots \\{\hat{h}}_{1,N_{R}} & {\hat{h}}_{2,N_{R}} & \cdots & {\hat{h}}_{N_{T},N_{R}}\end{bmatrix}.}} & {{Eq}\quad(51)}\end{matrix}$The matrix Ĥ may be provided by the channel MIMO/data processor 520within the same stage.

If the remodulated symbol stream corresponding to the i-th transmitantenna is expressed as {haeck over (x)}_(i), then the estimatedinterference component î ^(i) due to the recovered transmitted signalmay be expressed as: $\begin{matrix}{{\underset{\_}{\hat{i}}}^{i} = {\begin{bmatrix}\begin{matrix}\begin{matrix}{{\hat{h}}_{i,1} \cdot {\overset{\Cup}{x}}_{i}} \\{{\hat{h}}_{i,2} \cdot {\overset{\Cup}{x}}_{i}}\end{matrix} \\\vdots\end{matrix} \\{{\hat{h}}_{i,N_{R}} \cdot {\overset{\Cup}{x}}_{i}}\end{bmatrix}.}} & {{Eq}\quad(52)}\end{matrix}$

The N_(R) elements in the interference vector î ^(i) correspond to thecomponent of the received signal at each of the N_(R) receive antennasdue to symbol stream transmitted on the i-th transmit antenna. Eachelement of the vector represents an estimated component due to thedecoded data stream in the corresponding received modulation symbolstream. These components are interference to the remaining (not yetdetected) transmitted signals in the N_(R) received modulation symbolstreams (i.e., the vector r ^(k)) and are subtracted (i.e., canceled)from the received signal vector r ^(k) by a summer 812 to provide amodified vector r ^(k+1) having the components from the decoded datastream removed. This cancellation can be expressed as shown above inequation (5). The modified vector r ^(k+1) is provided as the inputvector to the next receiver processing stage, as shown in FIG. 5.

For a dispersive channel, the vector ĥ _(i) is replaced with an estimateof the channel transfer function vector defined in equation (23), ĥ_(i)(k), 0≦k≦L. Then the estimated interference vector at time n, î^(i)(n), may be expressed as: $\begin{matrix}{{{{\underset{\_}{\hat{i}}}^{i}(n)} = \begin{bmatrix}\begin{matrix}\begin{matrix}{\sum\limits_{k = 0}^{L}{{{\hat{h}}_{i1}(k)}{{\overset{\Cup}{x}}_{i}\left( {n - k} \right)}}} \\{\sum\limits_{k = 0}^{L}{{{\hat{h}}_{i2}(k)}{{\overset{\Cup}{x}}_{i}\left( {n - k} \right)}}}\end{matrix} \\\vdots\end{matrix} \\{\sum\limits_{k = 0}^{L}{{{\hat{h}}_{{iN}_{R}}(k)}{{\overset{\Cup}{x}}_{i}\left( {n - k} \right)}}}\end{bmatrix}},} & {{Eq}\quad(53)}\end{matrix}$where {haeck over (x)}_(i)(n) is the remodulated symbol for time n.Equation (54) effectively convolves the remodulated symbols with thechannel response estimates for each transmit-receive antenna pair.

For simplicity, the receiver architecture shown in FIG. 5 provides the(received or modified) modulation symbol streams to each receiverprocessing stage 510, and these streams have the interference componentsdue to previously decoded data streams removed (i.e., canceled). In theembodiment shown in FIG. 5, each stage removes the interferencecomponents due to the data stream decoded by that stage. In some otherdesign, the received modulation symbol streams may be provided to allstages, and each stage may perform the cancellation of interferencecomponents from all previously decoded data streams (which may beprovided from preceding stages). The interference cancellation may alsobe skipped for one or more stages (e.g., if the SNR for the data streamis high). Various modifications to the receiver architecture shown inFIG. 5 may be made and are within the scope of the invention.

Deriving and Reporting CSI

For simplicity, various aspects and embodiments of the invention havebeen described above wherein the CSI comprises SNR. In general, the CSImay comprise any type of information that is indicative of thecharacteristics of the communication link. Various types of informationmay be provided as CSI, some examples of which are described below.

In one embodiment, the CSI comprises signal-to-noise-plus-interferenceratio (SNR), which is derived as the ratio of the signal power over thenoise plus interference power. The SNR is typically estimated andprovided for each transmission channel used for data transmission (e.g.,each transmit data stream), although an aggregate SNR may also beprovided for a number of transmission channels. The SNR estimate may bequantized to a value having a particular number of bits. In oneembodiment, the SNR estimate is mapped to an SNR index, e.g., using alook-up table.

In another embodiment, the CSI comprises signal power and interferenceplus noise power. These two components may be separately derived andprovided for each transmission channel used for data transmission.

In yet another embodiment, the CSI comprises signal power, interferencepower, and noise power. These three components may be derived andprovided for each transmission channel used for data transmission.

In yet another embodiment, the CSI comprises signal-to-noise ratio plusa list of interference powers for each observable interference term.This information may be derived and provided for each transmissionchannel used for data transmission.

In yet another embodiment, the CSI comprises signal components in amatrix form (e.g., N_(T)×N_(R) complex entries for all transmit-receiveantenna pairs) and the noise plus interference components in matrix form(e.g., N_(T)×N_(R) complex entries). The transmitter unit may thenproperly combine the signal components and the noise plus interferencecomponents for the appropriate transmit-receive antenna pairs to derivethe quality for each transmission channel used for data transmission(e.g., the post-processed SNR for each transmitted data stream, asreceived at the receiver unit).

In yet another embodiment, the CSI comprises a data rate indicator forthe transmit data stream. The quality of a transmission channel to beused for data transmission may be determined initially (e.g., based onthe SNR estimated for the transmission channel) and a data ratecorresponding to the determined channel quality may then be identified(e.g., based on a look-up table). The identified data rate is indicativeof the maximum data rate that may be transmitted on the transmissionchannel for the required level of performance. The data rate is thenmapped to and represented by a data rate indicator (DRI), which can beefficiently coded. For example, if (up to) seven possible data rates aresupported by the transmitter unit for each transmit antenna, then a3-bit value may be used to represent the DRI where, e.g., a zero canindicate a data rate of zero (i.e., don't use the transmit antenna) and1 through 7 can be used to indicate seven different data rates. In atypical implementation, the quality measurements (e.g., SNR estimates)are mapped directly to the DRI based on, e.g., a look-up table.

In yet another embodiment, the CSI comprises an indication of theparticular processing scheme to be used at the transmitter unit for eachtransmit data stream. In this embodiment, the indicator may identify theparticular coding scheme and the particular modulation scheme to be usedfor the transmit data stream such that the desired level of performanceis achieved.

In yet another embodiment, the CSI comprises a differential indicatorfor a particular measure of quality for a transmission channel.Initially, the SNR or DRI or some other quality measurement for thetransmission channel is determined and reported as a referencemeasurement value. Thereafter, monitoring of the quality of thetransmission channel continues, and the difference between the lastreported measurement and the current measurement is determined. Thedifference may then be quantized to one or more bits, and the quantizeddifference is mapped to and represented by the differential indicator,which is then reported. The differential indicator may indicate toincrease or decrease the last reported measurement by a particular stepsize (or to maintain the last reported measurement). For example, thedifferential indicator may indicate that (1) the observed SNR for aparticular transmission channel has increased or decreased by aparticular step size, or (2) the data rate should be adjusted by aparticular amount, or some other change. The reference measurement maybe transmitted periodically to ensure that errors in the differentialindicators and/or erroneous reception of these indicators do notaccumulate.

Other forms of CSI may also be used and are within the scope of theinvention. In general, the CSI includes sufficient information inwhatever form that may be used to adjust the processing at thetransmitter such that the desired level of performance is achieved forthe transmitted data streams.

The CSI may be derived based on the signals transmitted from thetransmitter unit and received at the receiver unit. In an embodiment,the CSI is derived based on a pilot reference included in thetransmitted signals. Alternatively or additionally, the CSI may bederived based on the data included in the transmitted signals.

In yet another embodiment, the CSI comprises one or more signalstransmitted on the reverse link from the receiver unit to thetransmitter unit. In some systems, a degree of correlation may existbetween the forward and reverse links (e.g. time division duplexed (TDD)systems where the uplink and downlink share the same band in a timedivision multiplexed manner). In these systems, the quality of theforward link may be estimated (to a requisite degree of accuracy) basedon the quality of the reverse link, which may be estimated based onsignals (e.g., pilot signals) transmitted from the receiver unit. Thepilot signals would then represent a means for which the transmittercould estimate the CSI as observed by the receiver unit.

The signal quality may be estimated at the receiver unit based onvarious techniques. Some of these techniques are described in thefollowing patents, which are assigned to the assignee of the presentapplication and incorporated herein by reference:

-   -   U.S. Pat. No. 5,799,005, entitled “SYSTEM AND METHOD FOR        DETERMINING RECEIVED PILOT POWER AND PATH LOSS IN A CDMA        COMMUNICATION SYSTEM,” issued Aug. 25, 1998,    -   U.S. Pat. No. 5,903,554, entitled “METHOD AND APPARATUS FOR        MEASURING LINK QUALITY IN A SPREAD SPECTRUM COMMUNICATION        SYSTEM,” issued May 11, 1999,    -   U.S. Pat. Nos. 5,056,109, and 5,265,119, both entitled “METHOD        AND APPARATUS FOR CONTROLLING TRANSMISSION POWER IN A CDMA        CELLULAR MOBILE TELEPHONE SYSTEM,” respectively issued Oct. 8,        1991 and Nov. 23, 1993, and    -   U.S. Pat. No. 6,097,972, entitled “METHOD AND APPARATUS FOR        PROCESSING POWER CONTROL SIGNALS IN CDMA MOBILE TELEPHONE        SYSTEM,” issued Aug. 1, 2000.

Various types of information for CSI and various CSI reportingmechanisms are also described in U.S. Pat. No. 6,574,211, entitled“METHOD AND APPARATUS FOR HIGH RATE PACKET DATA TRANSMISSION,” filedNov. 3, 1997, and issued on Jun. 3, 2003 to Padovani et al. assigned tothe assignee of the present application, and in “TIE/EIA/IS-856 cdma2000High Rate Packet Data Air Interface Specification,” both of which areincorporated herein by reference.

The CSI may be reported back to the transmitter using various CSItransmission schemes. For example, the CSI may be sent in full,differentially, or a combination thereof. In one embodiment, CSI isreported periodically, and differential updates are sent based on theprior transmitted CSI. In another embodiment, the CSI is sent only whenthere is a change (e.g., if the change exceeds a particular threshold),which may lower the effective rate of the feedback channel. As anexample, the SNRs may be sent back (e.g., differentially) only when theychange. For an OFDM system (with or without MIMO), correlation in thefrequency domain may be exploited to permit reduction in the amount ofCSI to be fed back. As an example for an OFDM system, if the SNRcorresponding to a particular spatial subchannel for NM frequencysubchannels is the same, the SNR and the first and last frequencysubchannels for which this condition is true may be reported. Othercompression and feedback channel error recovery techniques to reduce theamount of data to be fed back for CSI may also be used and are withinthe scope of the invention.

Referring back to FIG. 1, the CSI (e.g., the channel SNR) determined byRX MIMO processor 156 is provided to a TX data processor 162, whichprocesses the CSI and provides processed data to one or more modulators154. Modulators 154 further condition the processed data and transmitthe CSI back to transmitter system 110 via a reverse channel.

At system 110, the transmitted feedback signal is received by antennas124, demodulated by demodulators 122, and provided to a RX dataprocessor 132. RX data processor 132 performs processing complementaryto that performed by TX data processor 162 and recovers the reportedCSI, which is then provided to, and used to adjust the processing by, TXdata processor 114 and TX MIMO processor 120.

Transmitter system 110 may adjust (i.e., adapt) its processing based onthe CSI (e.g., SNR information) from receiver system 150. For example,the coding for each transmission channel may be adjusted such that theinformation bit rate matches the transmission capability supported bythe channel SNR. Additionally, the modulation scheme for thetransmission channel may be selected based on the channel SNR. Otherprocessing (e.g., interleaving) may also be adjusted and are within thescope of the invention. The adjustment of the processing for eachtransmission channel based on the determined SNR for the channel allowsthe MIMO system to achieve high performance (i.e., high throughput orbit rate for a particular level of performance). The adaptive processingcan be applied to a single-carrier MIMO system or a multi-carrier basedMIMO system (e.g., a MIMO system utilizing OFDM).

The adjustment in the coding and/or the selection of the modulationscheme at the transmitter system may be achieved based on numeroustechniques, one of which is described in the aforementioned U.S. patentapplication Ser. No. 09/776,073.

MIMO System Operating Schemes

Various operating schemes may be implemented for a MIMO system thatemploys adaptive transmitter processing (which is dependent on theavailable CSI) and successive cancellation receiver processingtechniques described herein. Some of these operating schemes aredescribed in further detail below.

In one operating scheme, the coding and modulation scheme for eachtransmission channel is selected based on the channel's transmissioncapability, as determined by the channel's SNR. This scheme can provideimproved performance when used in combination with the successivecancellation receiver processing technique, as described in furtherdetail below. When there is a large disparity between the worst-case andbest-case transmission channels (i.e., transmit-receive antennapairings), the coding may be selected to introduce sufficient redundancyto allow the receiver system to recover the original data stream. Forexample, the worst transmit antenna may have associated with it a poorSNR at the receiver output. The forward error correction (FEC) code isthen selected to be powerful enough to allow the symbols transmittedfrom the worst-case transmit antenna to be correctly received at thereceiver system. In practice, improved error correction capability comesat the price of increased redundancy, which implies a sacrifice inoverall throughput. Thus, there is a tradeoff in terms of reducedthroughput for increased redundancy using FEC coding.

When the transmitter is provided with the SNR per recovered transmittedsignal, a different coding and/or modulation scheme may be used for eachtransmitted signal. For example, a specific coding and modulation schememay be selected for each transmitted signal based on its SNR so that theerror rates associated with the transmitted signals are approximatelyequal. In this way, throughput is not dictated by the SNR of theworst-case transmitted signal.

As an example, consider a 4×4 MIMO system with 4 transmit and 4 receiveantennas and employing the successive cancellation receiver processingtechnique described herein. For this example, the SNR for the fourtransmitted signals are 5 dB, 8.5 dB, 13 dB, and 17.5 dB. If the samecoding and modulation scheme is used for all four transmitted signal,the selected scheme would be dictated by the transmitted signal having 5dB SNR. Using the information given in Table 1, each transmit antennawould employ a coding rate of ¾ and QPSK modulation, giving a totalmodulation efficiency of 6 information bits/symbol, or 1.5 informationbits/symbol/transmitted signal.

With CSI available, the transmitter may select the following coding andmodulation schemes for the four transmitted signals, as shown in Table2. TABLE 2 SNR Modulation # of Information (dB) Coding Rate SymbolBits/Symbol 5 3/4 QPSK 1.5 8.5 5/8 16-QAM 2.5 13  7/12 64-QAM 3.5 17.55/6 64-QAM 5By adjusting the coding and modulation scheme at the transmitter basedon the available CSI, the effective modulation efficiency achieved ismore than doubled to 12.5 bits/symbol versus 6 bits/symbol without CSI.The decoded error rate for each of the transmitted signals will beapproximately equal since the coding and modulation scheme was selectedto achieve this level of performance.

With adaptive processing at the transmitter system based on theavailable CSI, the successive cancellation receiver processing techniquemay be altered to take advantage of the fact that the bit error ratesfor the transmitted signals are approximately equal. If the coding andmodulation scheme used on each transmitted signal provides an equivalentdecoded error rate, then the ranking procedure (i.e., highest to lowerSNR) may be omitted from the receiver processing, which may simplify theprocessing. In practical implementation, there may be slight differencesin the decoded error rates for the transmitted signals. In this case,the SNR for the transmitted signals (after the linear or non-linearprocessing) may be ranked and the best post-processed SNR selected fordetection (i.e., demodulation and decoding) first, as described above.

With CSI available at the transmitter, throughput is no longer dictatedby the worst-case transmitted signal since the coding and modulationschemes are selected to provide a particular level of performance (e.g.,a particular BER) on each transmission channel based on the channel'sSNR. Since FEC coding is applied to each transmission channelindependently, the minimum amount of redundancy required to meet thetarget level of performance is used, and throughput is maximized. Theperformance achievable with adaptive transmitter processing based on CSI(e.g., SNR) and successive cancellation receiver processing rivals thatof a full-CSI processing scheme (whereby full characterization isavailable for each transmit-receive antenna pair) under certainoperating conditions, as described in further detail below.

In another operating scheme, the transmitter it not provided with theSNR achieved for each transmission channel, but may be provided with asingle value indicative of the average SNR for all transmissionchannels, or possibly some information indicating which transmitantennas to be used for data transmission. In this scheme, thetransmitter may employ the same coding and modulation scheme on alltransmit antennas used for data transmission, which may be a subset ofthe N_(T) available transmit antennas. When the same coding andmodulation scheme is used on all transmit antennas, performance may becompromised. This is because the overall performance of the successivecancellation receiver processing technique is dependent on the abilityto decode each transmitted signal error free. This correct detection isimportant to effectively cancel the interference due to the recoveredtransmitted signal.

By using same coding and modulation scheme for all transmitted signals,the recovered transmitted signal with the worst SNR will have thehighest decoded error rate. This ultimately limits the performance ofthe MIMO system since the coding and modulation scheme is selected sothat the error rate associated with the worst-case transmitted signalmeets the overall error rate requirements. To improve efficiency,additional receive antennas may be used to provide improved error rateperformance on the first recovered transmitted signal. By employ morereceive antennas than transmit antennas, the error rate performance ofthe first recovered transmitted signal has a diversity order of(N_(R)−N_(T)+1) and reliability is increased.

In yet another operating scheme, the transmitted data streams are“cycled” across all available transmit antennas. This scheme improvesthe SNR statistics for each of the recovered transmitted signals sincethe transmitted data is not subjected to the worst-case transmissionchannel, but instead is subjected to all transmission channels. Thedecoder associated with a specific data stream is effectively presentedwith “soft decisions” that are representative of the average across allpossible pairs of transmit-receive antennas. This operating scheme isdescribed in further detail in European Patent Application Serial No.99302692.1, entitled “WIRELESS COMMUNICATIONS SYSTEM HAVING A SPACE-TIMEARCHITECTURE EMPLOYING MULTI-ELEMENT ANTENNAS AT BOTH THE TRANSMITTERAND RECEIVER,” and incorporated herein by reference.

The successive cancellation receiver processing technique allows a MIMOsystem to utilize the additional dimensionalities created by the use ofmultiple transmit and receive antennas, which is a main advantage foremploying MIMO. Depending on the characteristics of the MIMO channel, alinear spatial equalization technique (e.g., CCMI or MMSE) or aspace-time equalization technique (e.g., MMSE-LE, DFE, or MLSE) may beused to process the received signals. The successive cancellationreceiver processing technique, when used in combination with theadaptive transmitter processing based on the available CSI, may allowthe same number of modulation symbols to be transmitted for each timeslot as for a MIMO system utilizing full CSI.

Other linear and non-linear receiver processing techniques may also beused in conjunction with the successive cancellation receiver processingtechnique and the adaptive transmitter processing technique, and this iswithin the scope of the invention. Analogously, FIGS. 6A through 6Crepresent embodiments of three receiver processing technique capable ofprocessing a MIMO transmission and determining the characteristics ofthe transmission channels (i.e., the SNR). Other receiver designs basedon the techniques presented herein and other receiver processingtechniques can be contemplated and are within the scope of theinvention.

The linear and non-linear receiver processing techniques (e.g., CCMI,MMSE, MMSE-LE, DFE, MLSE, and other techniques) may also be used in astraightforward manner without adaptive processing at the transmitterwhen only the overall received signal SNR or the attainable overallthroughput estimated based on such SNR is feed back. In oneimplementation, a modulation format is determined based on the receivedSNR estimate or the estimated throughput, and the same modulation formatis used for all transmission channels. This method may reduce theoverall system throughput but may also greatly reduce the amount ofinformation sent back over the reverse link.

System Performance

Improvement in system performance may be realized with the use of thesuccessive cancellation receiver processing technique and the adaptivetransmitter processing technique based on the available CSI. The systemthroughput with CSI feedback can be computed and compared against thethroughput with full CSI feedback. The system throughput can be definedas: $\begin{matrix}{{C = {\sum\limits_{i = 1}^{N_{C}}{\log_{2}\left( {1 + \gamma_{i}} \right)}}},} & {{Eq}\quad(54)}\end{matrix}$where γ_(i) is the SNR of each received modulation symbol. The SNR forsome of the receiver processing techniques are summarized above.

FIG. 9A shows the improvement in SNR for a 4×4 MIMO channelconfiguration using the successive cancellation receiver processingtechnique. The results are obtained from a computer simulation. In thesimulation, the following assumptions are made: (1) independent Rayleighfading channels between receiver-transmit antenna pairs (i.e., no arraycorrelation), (2) total interference cancellation (i.e., no decisionerrors are made in the decoding process and accurate channel estimatesare available at the receiver). In practical implementation, channelestimates are not totally accurate, and a back-off factor may be used inthe modulation scheme selected for each transmitted data stream. Inaddition, some decision errors are likely to occur in the detection ofeach transmitted data stream. This probability can be reduced ifindependently transmitted data streams are individually coded, whichwould then allow the receiver to decode the data streams independently,which may then reduce the probability of decision errors. In this case,the decoded data is re-encoded to construct the interference estimateused in the successive interference cancellation.

As shown in FIG. 9A, the first recovered transmitted signal has thepoorest SNR distribution. Each subsequent recovered transmitted signalhas improved SNR distributions, with the final recovered transmittedsignal (i.e., the fourth one in this example) having the best overallSNR distribution. The distribution of the average SNR formed by summingthe SNRs for the individual transmitted signals and dividing by four isalso shown. The SNR distribution achieved without successive spatialequalization and interference cancellation is given by the SNRdistribution for the first recovered transmitted signal. In comparingthe SNR distribution for the first recovered transmitted signal to theaverage SNR distribution, it can be seen that the spatial equalizationand interference cancellation technique improves the effective SNR atthe receiver.

FIG. 9B shows the average throughput for a number of receive processingtechniques, including (1) the linear spatial equalization technique(without interference cancellation), (2) the spatial equalization andinterference cancellation technique, and (3) the full-CSI technique. Foreach of these schemes, the transmitter is provided with either full orpartial CSI for all transmitted signals, and the data for eachtransmitted signal is encoded and modulated based on the SNR. For theplots shown in FIG. 9B, the CCMI and MMSE techniques are used for thelinear spatial equalization technique.

FIG. 9B shows the theoretical capacity (plot 920) achieved when usingfull-CSI processing based on the decomposition of the MIMO channel intoeigenmodes. FIG. 9B further shows that the throughputs for both the CCMItechnique (plot 924) and MMSE technique (plot 922) with partial-CSI butwithout interference cancellation have lower throughput than thecapacity bound (plot 920).

Since capacity is proportional to SNR, as shown in equation (20), andSNR improves with the use of successive interference cancellation,capacity on average improves using the spatial equalization andinterference cancellation technique. Using spatial equalization (withCCMI) and interference cancellation technique and partial-CSI, thethroughput (plot 926) is improved over the spatial equalization onlyschemes (plots 922 and 924), with performance improving more as SNRincreases. Using spatial equalization (with MMSE) and interferencecancellation technique and partial-CSI, the throughput (plot 928) isidentical to the capacity bound (plot 920), which represents remarkablesystem performance. Plot 920 assumes perfect channel estimates and nodecision errors. The throughput estimates shown in FIG. 9B for thesuccessive spatial equalization and interference cancellation techniquewith partial-CSI processing may degrade under practical implementationsdue to imperfect interference cancellation and detection errors.

FIG. 9C shows the average throughput for the successive space-timeequalization (with MMSE-LE) and interference cancellation technique withadaptive transmitter processing based on CSI for a 4×4 MIMO system. Theplots are obtained by averaging over a large number of staticrealizations of a dispersive channel model (i.e., VehA). FIG. 9C showsthe capacity bound (plot 930) and the performance of the MMSE-LEtechnique with interference cancellation (plot 934) and withoutsuccessive interference cancellation (plot 932). The throughputperformance for the MMSE-LE without successive interference cancellationtechnique (plot 932) degrades at higher SNR values. The throughputperformance for the MMSE-LE with successive interference cancellationtechnique (plot 934) is close to channel capacity, which represents ahigh level of performance.

The elements of the transmitter and receiver systems may be implementedwith one or more digital signal processors (DSP), application specificintegrated circuits (ASIC), processors, microprocessors, controllers,microcontrollers, field programmable gate arrays (FPGA), programmablelogic devices, other electronic units, or any combination thereof. Someof the functions and processing described herein may also be implementedwith software executed on a processor.

Certain aspects of the invention may be implemented with a combinationof software and hardware. For example, computations for the symbolestimates for the linear spatial equalization, the space-timeequalization, and the derivation of the channel SNR may be performedbased on program codes executed on a processor (controllers 540 in FIG.5).

For clarity, the receiver architecture shown in FIG. 5 includes a numberof receiving processing stages, one stage for each data stream to bedecoded. In some implementations, these multiple stages may beimplemented with a single hardware unit or a single software module thatis re-executed for each stage. In this manner, the hardware or softwaremay be time shared to simplify the receiver design.

Headings are included herein for reference and to aid in the locatingcertain sections. These heading are not intended to limit the scope ofthe concepts described therein under, and these concepts may haveapplicability in other sections throughout the entire specification.

The previous description of the disclosed embodiments is provided toenable any person skilled in the art to make or use the presentinvention. Various modifications to these embodiments will be readilyapparent to those skilled in the art, and the generic principles definedherein may be applied to other embodiments without departing from thespirit or scope of the invention. Thus, the present invention is notintended to be limited to the embodiments shown herein but is to beaccorded the widest scope consistent with the principles and novelfeatures disclosed herein.

1. A method for processing data at a receiver unit in a multiple-inputmultiple-output (MIMO) communication system, comprising: processing aplurality of input signals having included therein one or more symbolstreams corresponding to one or more data streams to provide a decodeddata stream for one of the one or more symbol streams, wherein theplurality of input signals were transmitted over the MIMO communicationsystem using orthogonal frequency division multiplexing (OFDM); derivinga plurality of modified signals based on the input signals and havingcomponents due to the decoded data stream approximately removed;performing the processing and selectively performing the deriving foreach of one or more iterations, one iteration for each data stream to bedecoded, and wherein the input signals for each iteration subsequent toa first iteration are the modified signals from a preceding iteration;and determining channel state information (CSI) indicative ofcharacteristics of a channel used for transmitting the data steams,wherein the data streams are adaptively processed at a transmitter unitbased in part on the CSI.
 2. The method of claim 1, wherein the derivingis omitted for a last iteration.
 3. The method of claim 1, wherein theprocessing includes processing the input signals in accordance with aparticular receive processing scheme to provide the one or more symbolstreams, and processing a selected one of the one or more symbol streamsto provide the decoded data stream.
 4. The method of claim 3, furthercomprising: for each iteration, estimating a quality of each of one ormore unprocessed symbol streams included in the input signals; andselecting one unprocessed symbol stream for processing based onestimated qualities for the one or more unprocessed symbol streams. 5.The method of claim 4, wherein the quality of each unprocessed symbolstream is estimated based on a signal-to-noise-plus-interference-ratio(SNR).
 6. The method of claim 4, wherein the unprocessed symbol streamhaving the best estimated quality is selected for processing.
 7. Themethod of claim 3, wherein the receive processing scheme performs linearspatial processing on the input signals.
 8. The method of claim 7,wherein the receive processing scheme implements a channel correlationmatrix inversion (CCMI) technique.
 9. The method of claim 7, wherein thereceive processing scheme implements a minimum mean square error (MMSE)technique
 10. The method of claim 7, wherein the receive processingscheme implements a full-CSI processing technique
 11. The method ofclaim 3, wherein the receive processing scheme performs space-timeprocessing on the input signals.
 12. The method of claim 11, wherein thereceive processing scheme implements a minimum mean-square error linearspace-time equalizer (MMSE-LE).
 13. The method of claim 11, wherein thereceive processing scheme implements a decision feedback space-timeequalizer (DFE).
 14. The method of claim 11, wherein the receiveprocessing scheme implements a maximum-likelihood sequence estimator(MLSE).
 15. The method of claim 1, wherein the deriving includesgenerating a remodulated symbol stream based on the decoded data stream;forming a plurality of interference signals based on the remodulatedsymbol stream; and removing the interference signals from the inputsignals to derive the modified signals that serve as input signals for asucceeding iteration.
 16. The method of claim 15, wherein theinterference signals are formed based on a channel coefficient matrix Hindicative of characteristics of the channel.
 17. The method of claim 1,further comprising: transmitting the CSI from the receiver unit to thetransmitter unit.
 18. The method of claim 1, wherein the CSI comprisessignal-to-noise-plus-interference-ratio (SNR) estimates for at least oneof one or more transmission channels.
 19. The method of claim 1, whereinthe CSI comprises characterizations for one or more transmissionchannels.
 20. The method of claim 1, wherein the CSI comprises anindication of a particular data rate supported by at least one of one ormore transmission channels used for data transmission.
 21. The method ofclaim 1, wherein the CSI comprises an indication of a particularprocessing scheme to be used for at least one of one or moretransmission channels.
 22. The method of claim 1, wherein the CSIcomprises signal measurements and noise plus interference measurementsfor one or more transmission channels.
 23. The method of claim 1,wherein the CSI comprises signal measurements, noise measurements, andinterference measurements for one or more transmission channels.
 24. Themethod of claim 1, wherein the CSI comprises signal to noise ratio andinterference measurements for one or more transmission channels.
 25. Themethod of claim 1, wherein the CSI comprises signal components and noiseplus interference components for one or more transmission channels. 26.The method of claim 1, wherein the CSI comprises indications of changesin the characteristics of one or more transmission channels.
 27. Themethod of claim 1, wherein the CSI is determined at the receiver unitand reported to the transmitter unit.
 28. The method of claim 1, whereinthe CSI is determined at the transmitter unit based on one or moresignals transmitted by the receiver unit.
 29. The method of claim 1,wherein each data stream is coded at the transmitter unit in accordancewith a coding scheme selected based on the CSI for the transmissionchannel used to transmit the data stream.
 30. The method of claim 29,wherein each data stream is independently coded in accordance with acoding scheme selected based on the CSI for the transmission channelused to transmit the data stream.
 31. The method of claim 29, whereineach data stream is further modulated in accordance with a modulationscheme selected based on the CSI for the transmission channel used totransmit the data stream.
 32. The method of claim 31, wherein the codingand modulation schemes are selected at the transmitter unit based on theCSI.
 33. The method of claim 32, wherein the coding and modulationschemes are indicated by the CSI.
 34. The method of claim 3, wherein theprocessing of the selected symbol stream includes demodulating thesymbol stream to provide demodulated symbols, and decoding thedemodulated symbols to provide the decoded data stream.
 35. The methodof claim 34, wherein the processing of the selected symbol streamfurther includes deinterleaving the demodulated symbols, wherein thedecoding is performed on the deinterleaved symbols to provide thedecoded data stream.
 36. The method of claim 1, wherein the processingat the receiver unit is independently performed for each of a pluralityof frequency subchannels.
 37. A method for processing data at a receiverunit in a multiple-input multiple-output (MIMO) communication system,comprising: receiving a plurality of signals via a plurality of receivedantennas, wherein the plurality of input signals were transmitted overthe MIMO communication system using orthogonal frequency divisionmultiplexing (OFDM); processing the received signals in accordance witha particular receive processing scheme to provide a plurality of symbolstreams corresponding to a plurality of transmitted data streams;processing a selected one of the symbol streams to provide a decodeddata stream; forming a plurality of interference signals based on thedecoded data stream; deriving a plurality of modified signals based onthe received signals and the interference signals; performing theprocessing of the received signals and the selected symbol stream andselectively performing the forming and deriving for one or moreiterations, one iteration each transmitted data stream to be decoded,wherein a first iteration is performed on the received signals and eachsubsequent iteration is performed on the modified signals from apreceding iteration; and determining channel state information (CSI)indicative of characteristics of a channel used for transmitting thedata steams, wherein the data streams are adaptively processed at atransmitter unit based in part on the CSI.
 38. A method forcommunicating data from a transmitter unit to a receiver unit in amultiple-input multiple-output (MIMO) communication system, comprising:at the receiver unit, receiving a plurality of signals via a pluralityof receive antennas, wherein each received signal comprises acombination of one or more signals transmitted from the transmitterunit, and further wherein the plurality of signals were transmitted overthe MIMO communication system using orthogonal frequency divisionmultiplexing (OFDM), processing the received signals in accordance witha successive cancellation receiver processing technique to provide aplurality of decoded data streams transmitted from the transmitter unit,determining channel state information (CSI) indicative ofcharacteristics of a channel used to transmit the data steams, andtransmitting the CSI back to the transmitter unit; and at thetransmitter unit, adaptively processing each data stream prior totransmission over the channel in accordance with the received CSI. 39.The method of claim 38, wherein the successive cancellation receiverprocessing scheme performs a plurality of iterations to provide thedecoded data streams, one iteration for each decoded data stream. 40.The method of claim 39, wherein each iteration includes processing aplurality of input signals in accordance with a particular linear ornon-linear processing scheme to provide one or more symbol streams,processing a selected one of the one or more symbol streams to provide adecoded data stream, and deriving a plurality of modified signals basedon the input signals and having components due to the decoded datastream approximately removed, wherein the input signals for a firstiteration are the received signals and the input signals for eachsubsequent iteration are the modified signals from a precedingiteration.
 41. The method of claim 38, wherein the CSI comprises asignal-to-noise-plus-interference-ratio (SNR) for at least one of one ormore transmission channels.
 42. The method of claim 38, wherein the CSIcomprises an indication of a particular data rate supported by at leastone of one or more transmission channels.
 43. The method of claim 38,wherein the CSI comprises an indication of a particular processingscheme to be used for at least one of one or more transmission channels.44. The method of claim 38, wherein the adaptive processing at thetransmitter unit includes encoding a data stream in accordance with aparticular coding scheme selected based on the CSI associated with thedata stream.
 45. The method of claim 44, wherein the adaptive processingat the transmitter unit further includes modulating the encoded datastream in accordance with a particular modulation scheme selected basedon the CSI associated with the data stream.
 46. A multiple-inputmultiple-output (MIMO) communication system, comprising: a receiver unitcomprising a plurality of front-end processors configured to process aplurality of received signals to provide a plurality of symbol streams,wherein the plurality of received signals were transmitted over the MIMOcommunication system using orthogonal frequency division multiplexing(OFDM), at least one receive processor coupled to the front-endprocessors and configured to process the symbol streams in accordancewith a successive cancellation receiver processing scheme to provide aplurality of decoded data streams, and to further derive channel stateinformation (CSI) indicative of characteristics of a channel used totransmit the data streams, and a transmit data processor operativelycoupled to the receive processor and configured to process the CSI fortransmission back to the transmitter unit; and a transmitter unitcomprising at least one demodulator configured to receive and processone or more signals from the receiver unit to recover the transmittedCSI, and a transmit data processor configured to adaptively process datafor transmission to the receiver unit based on the recovered CSI.
 47. Areceiver unit in a multiple-input multiple-output (MIMO) communicationsystem, comprising: a plurality of front-end processors configured toprocess a plurality of received signals to provide a plurality ofreceived symbol streams, wherein the plurality of received signals weretransmitted over the MIMO communication system using orthogonalfrequency division multiplexing (OFDM); at least one receive processorcoupled to the front-end processors and configured to process thereceived symbol streams to provide a plurality of decoded data streams,each receive processor including a plurality of processing stages, eachstage configured to process input symbol streams to provide a respectivedecoded data stream and channel state information (CSI) associated withthe decoded data stream, and to selectively provide modified symbolstreams for a succeeding stage, wherein the input symbol streams foreach stage are either the received symbol streams or the modified symbolstreams from a preceding stage; and a transmit processor configured toreceive and process the CSI associated with the decoded data streams fortransmission from the receiver unit, wherein the data streams areadaptively processed prior to transmission based in part on the CSI. 48.The receiver unit of claim 47, wherein each processing stage except alast stage includes a channel processor configured to process the inputsymbol streams to provide a decoded data stream, and an interferencecanceller configured to derive the modified symbol streams based on thedecoded data stream and the input symbol streams.
 49. The receiver unitof claim 48, wherein each channel processor includes an input processorconfigured to process the input symbol streams to provide a recoveredsymbol stream, and a data processor configured to process the recoveredsymbol stream to provide the decoded data stream.
 50. The receiver unitof claim 49, wherein each input processor includes a first processorconfigured to process the input symbol streams in accordance with alinear or non-linear receive processing scheme to provide the recoveredsymbol stream, and a channel quality estimator configured to estimate aquality of the recovered symbol stream.
 51. The receiver unit of claim50, wherein the estimated quality comprises asignal-to-noise-plus-interference-ratio (SNR).
 52. The receiver unit ofclaim 50, wherein the channel quality estimator is further configured toprovide an indication of a data rate supported for the recovered symbolstream based on the quality estimate.
 53. The receiver unit of claim 50,wherein the channel quality estimator is further configured to providean indication of a particular processing scheme to be used at atransmitter unit for the recovered symbol stream based on the qualityestimate.
 54. The receiver unit of claim 50, wherein the estimatedquality comprises an error signal indicative of detected noise plusinterference level at the output of the receiver unit.
 55. The receiverunit of claim 50, wherein the first processor performs linear spatialprocessing on the input symbol streams.
 56. The receiver unit of claim50, wherein the first processor performs space-time processing on theinput symbol streams.